Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.
With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
Competition's link: https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques
Remember: Any information that i state about a feature is present in data_descripition.txt
import numpy as np
import pandas as pd
pd.set_option('display.max_columns',None) # 3shan 22dr ashouf koul al colums
pd.set_option('display.max_row',100) # 3shan 22dr ashouf koul al rows
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
%matplotlib inline
# https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import KFold, cross_val_score
import scipy.stats
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
# almktba de gamda w hnshoof ht3ml eh 2odam hwa mn asm al methods algybnha mn gwaha byna alhya compare models
from pycaret.regression import setup, compare_models, predict_model
from catboost import CatBoostRegressor
import plotly.graph_objects as go
import plotly.express as px
import scipy.stats as stats
from IPython.display import display, HTML
from sklearn.linear_model import LinearRegression
train0 = pd.read_csv('train.csv')
test0 = pd.read_csv('test.csv')
sample_submission = pd.read_csv('sample_submission.csv')
print("Train_data shape = ",train0.shape)
print("Test_data shape = ",test0.shape)
Train_data shape = (1460, 81) Test_data shape = (1459, 80)
train0.head()
| Id | MSSubClass | MSZoning | LotFrontage | LotArea | Street | Alley | LotShape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | Condition2 | BldgType | HouseStyle | OverallQual | OverallCond | YearBuilt | YearRemodAdd | RoofStyle | RoofMatl | Exterior1st | Exterior2nd | MasVnrType | MasVnrArea | ExterQual | ExterCond | Foundation | BsmtQual | BsmtCond | BsmtExposure | BsmtFinType1 | BsmtFinSF1 | BsmtFinType2 | BsmtFinSF2 | BsmtUnfSF | TotalBsmtSF | Heating | HeatingQC | CentralAir | Electrical | 1stFlrSF | 2ndFlrSF | LowQualFinSF | GrLivArea | BsmtFullBath | BsmtHalfBath | FullBath | HalfBath | BedroomAbvGr | KitchenAbvGr | KitchenQual | TotRmsAbvGrd | Functional | Fireplaces | FireplaceQu | GarageType | GarageYrBlt | GarageFinish | GarageCars | GarageArea | GarageQual | GarageCond | PavedDrive | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | PoolQC | Fence | MiscFeature | MiscVal | MoSold | YrSold | SaleType | SaleCondition | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 60 | RL | 65.0 | 8450 | Pave | NaN | Reg | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | 7 | 5 | 2003 | 2003 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 196.0 | Gd | TA | PConc | Gd | TA | No | GLQ | 706 | Unf | 0 | 150 | 856 | GasA | Ex | Y | SBrkr | 856 | 854 | 0 | 1710 | 1 | 0 | 2 | 1 | 3 | 1 | Gd | 8 | Typ | 0 | NaN | Attchd | 2003.0 | RFn | 2 | 548 | TA | TA | Y | 0 | 61 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 2 | 2008 | WD | Normal | 208500 |
| 1 | 2 | 20 | RL | 80.0 | 9600 | Pave | NaN | Reg | Lvl | AllPub | FR2 | Gtl | Veenker | Feedr | Norm | 1Fam | 1Story | 6 | 8 | 1976 | 1976 | Gable | CompShg | MetalSd | MetalSd | None | 0.0 | TA | TA | CBlock | Gd | TA | Gd | ALQ | 978 | Unf | 0 | 284 | 1262 | GasA | Ex | Y | SBrkr | 1262 | 0 | 0 | 1262 | 0 | 1 | 2 | 0 | 3 | 1 | TA | 6 | Typ | 1 | TA | Attchd | 1976.0 | RFn | 2 | 460 | TA | TA | Y | 298 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 5 | 2007 | WD | Normal | 181500 |
| 2 | 3 | 60 | RL | 68.0 | 11250 | Pave | NaN | IR1 | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | 7 | 5 | 2001 | 2002 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 162.0 | Gd | TA | PConc | Gd | TA | Mn | GLQ | 486 | Unf | 0 | 434 | 920 | GasA | Ex | Y | SBrkr | 920 | 866 | 0 | 1786 | 1 | 0 | 2 | 1 | 3 | 1 | Gd | 6 | Typ | 1 | TA | Attchd | 2001.0 | RFn | 2 | 608 | TA | TA | Y | 0 | 42 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 9 | 2008 | WD | Normal | 223500 |
| 3 | 4 | 70 | RL | 60.0 | 9550 | Pave | NaN | IR1 | Lvl | AllPub | Corner | Gtl | Crawfor | Norm | Norm | 1Fam | 2Story | 7 | 5 | 1915 | 1970 | Gable | CompShg | Wd Sdng | Wd Shng | None | 0.0 | TA | TA | BrkTil | TA | Gd | No | ALQ | 216 | Unf | 0 | 540 | 756 | GasA | Gd | Y | SBrkr | 961 | 756 | 0 | 1717 | 1 | 0 | 1 | 0 | 3 | 1 | Gd | 7 | Typ | 1 | Gd | Detchd | 1998.0 | Unf | 3 | 642 | TA | TA | Y | 0 | 35 | 272 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 2 | 2006 | WD | Abnorml | 140000 |
| 4 | 5 | 60 | RL | 84.0 | 14260 | Pave | NaN | IR1 | Lvl | AllPub | FR2 | Gtl | NoRidge | Norm | Norm | 1Fam | 2Story | 8 | 5 | 2000 | 2000 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 350.0 | Gd | TA | PConc | Gd | TA | Av | GLQ | 655 | Unf | 0 | 490 | 1145 | GasA | Ex | Y | SBrkr | 1145 | 1053 | 0 | 2198 | 1 | 0 | 2 | 1 | 4 | 1 | Gd | 9 | Typ | 1 | TA | Attchd | 2000.0 | RFn | 3 | 836 | TA | TA | Y | 192 | 84 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 12 | 2008 | WD | Normal | 250000 |
# test -> is the same train data but not have SalePrice da altby3y y3ny (●'◡'●)
test0.head()
| Id | MSSubClass | MSZoning | LotFrontage | LotArea | Street | Alley | LotShape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | Condition2 | BldgType | HouseStyle | OverallQual | OverallCond | YearBuilt | YearRemodAdd | RoofStyle | RoofMatl | Exterior1st | Exterior2nd | MasVnrType | MasVnrArea | ExterQual | ExterCond | Foundation | BsmtQual | BsmtCond | BsmtExposure | BsmtFinType1 | BsmtFinSF1 | BsmtFinType2 | BsmtFinSF2 | BsmtUnfSF | TotalBsmtSF | Heating | HeatingQC | CentralAir | Electrical | 1stFlrSF | 2ndFlrSF | LowQualFinSF | GrLivArea | BsmtFullBath | BsmtHalfBath | FullBath | HalfBath | BedroomAbvGr | KitchenAbvGr | KitchenQual | TotRmsAbvGrd | Functional | Fireplaces | FireplaceQu | GarageType | GarageYrBlt | GarageFinish | GarageCars | GarageArea | GarageQual | GarageCond | PavedDrive | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | PoolQC | Fence | MiscFeature | MiscVal | MoSold | YrSold | SaleType | SaleCondition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1461 | 20 | RH | 80.0 | 11622 | Pave | NaN | Reg | Lvl | AllPub | Inside | Gtl | NAmes | Feedr | Norm | 1Fam | 1Story | 5 | 6 | 1961 | 1961 | Gable | CompShg | VinylSd | VinylSd | None | 0.0 | TA | TA | CBlock | TA | TA | No | Rec | 468.0 | LwQ | 144.0 | 270.0 | 882.0 | GasA | TA | Y | SBrkr | 896 | 0 | 0 | 896 | 0.0 | 0.0 | 1 | 0 | 2 | 1 | TA | 5 | Typ | 0 | NaN | Attchd | 1961.0 | Unf | 1.0 | 730.0 | TA | TA | Y | 140 | 0 | 0 | 0 | 120 | 0 | NaN | MnPrv | NaN | 0 | 6 | 2010 | WD | Normal |
| 1 | 1462 | 20 | RL | 81.0 | 14267 | Pave | NaN | IR1 | Lvl | AllPub | Corner | Gtl | NAmes | Norm | Norm | 1Fam | 1Story | 6 | 6 | 1958 | 1958 | Hip | CompShg | Wd Sdng | Wd Sdng | BrkFace | 108.0 | TA | TA | CBlock | TA | TA | No | ALQ | 923.0 | Unf | 0.0 | 406.0 | 1329.0 | GasA | TA | Y | SBrkr | 1329 | 0 | 0 | 1329 | 0.0 | 0.0 | 1 | 1 | 3 | 1 | Gd | 6 | Typ | 0 | NaN | Attchd | 1958.0 | Unf | 1.0 | 312.0 | TA | TA | Y | 393 | 36 | 0 | 0 | 0 | 0 | NaN | NaN | Gar2 | 12500 | 6 | 2010 | WD | Normal |
| 2 | 1463 | 60 | RL | 74.0 | 13830 | Pave | NaN | IR1 | Lvl | AllPub | Inside | Gtl | Gilbert | Norm | Norm | 1Fam | 2Story | 5 | 5 | 1997 | 1998 | Gable | CompShg | VinylSd | VinylSd | None | 0.0 | TA | TA | PConc | Gd | TA | No | GLQ | 791.0 | Unf | 0.0 | 137.0 | 928.0 | GasA | Gd | Y | SBrkr | 928 | 701 | 0 | 1629 | 0.0 | 0.0 | 2 | 1 | 3 | 1 | TA | 6 | Typ | 1 | TA | Attchd | 1997.0 | Fin | 2.0 | 482.0 | TA | TA | Y | 212 | 34 | 0 | 0 | 0 | 0 | NaN | MnPrv | NaN | 0 | 3 | 2010 | WD | Normal |
| 3 | 1464 | 60 | RL | 78.0 | 9978 | Pave | NaN | IR1 | Lvl | AllPub | Inside | Gtl | Gilbert | Norm | Norm | 1Fam | 2Story | 6 | 6 | 1998 | 1998 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 20.0 | TA | TA | PConc | TA | TA | No | GLQ | 602.0 | Unf | 0.0 | 324.0 | 926.0 | GasA | Ex | Y | SBrkr | 926 | 678 | 0 | 1604 | 0.0 | 0.0 | 2 | 1 | 3 | 1 | Gd | 7 | Typ | 1 | Gd | Attchd | 1998.0 | Fin | 2.0 | 470.0 | TA | TA | Y | 360 | 36 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 6 | 2010 | WD | Normal |
| 4 | 1465 | 120 | RL | 43.0 | 5005 | Pave | NaN | IR1 | HLS | AllPub | Inside | Gtl | StoneBr | Norm | Norm | TwnhsE | 1Story | 8 | 5 | 1992 | 1992 | Gable | CompShg | HdBoard | HdBoard | None | 0.0 | Gd | TA | PConc | Gd | TA | No | ALQ | 263.0 | Unf | 0.0 | 1017.0 | 1280.0 | GasA | Ex | Y | SBrkr | 1280 | 0 | 0 | 1280 | 0.0 | 0.0 | 2 | 0 | 2 | 1 | Gd | 5 | Typ | 0 | NaN | Attchd | 1992.0 | RFn | 2.0 | 506.0 | TA | TA | Y | 0 | 82 | 0 | 0 | 144 | 0 | NaN | NaN | NaN | 0 | 1 | 2010 | WD | Normal |
sample_submission
| Id | SalePrice | |
|---|---|---|
| 0 | 1461 | 169277.052498 |
| 1 | 1462 | 187758.393989 |
| 2 | 1463 | 183583.683570 |
| 3 | 1464 | 179317.477511 |
| 4 | 1465 | 150730.079977 |
| ... | ... | ... |
| 1454 | 2915 | 167081.220949 |
| 1455 | 2916 | 164788.778231 |
| 1456 | 2917 | 219222.423400 |
| 1457 | 2918 | 184924.279659 |
| 1458 | 2919 | 187741.866657 |
1459 rows × 2 columns
target = train0['SalePrice']
train1 = train0.drop(['Id','SalePrice'], axis=1) # hndrop mn hna al target bt3na whwa al SalePrice and Id 3shan lma negy n3ml concat m3 al test 2odam wkman doul msh 3yznhom dlw2ty
test_ids = test0['Id']
test1 = test0.drop('Id', axis=1)
train1.head()
| MSSubClass | MSZoning | LotFrontage | LotArea | Street | Alley | LotShape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | Condition2 | BldgType | HouseStyle | OverallQual | OverallCond | YearBuilt | YearRemodAdd | RoofStyle | RoofMatl | Exterior1st | Exterior2nd | MasVnrType | MasVnrArea | ExterQual | ExterCond | Foundation | BsmtQual | BsmtCond | BsmtExposure | BsmtFinType1 | BsmtFinSF1 | BsmtFinType2 | BsmtFinSF2 | BsmtUnfSF | TotalBsmtSF | Heating | HeatingQC | CentralAir | Electrical | 1stFlrSF | 2ndFlrSF | LowQualFinSF | GrLivArea | BsmtFullBath | BsmtHalfBath | FullBath | HalfBath | BedroomAbvGr | KitchenAbvGr | KitchenQual | TotRmsAbvGrd | Functional | Fireplaces | FireplaceQu | GarageType | GarageYrBlt | GarageFinish | GarageCars | GarageArea | GarageQual | GarageCond | PavedDrive | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | PoolQC | Fence | MiscFeature | MiscVal | MoSold | YrSold | SaleType | SaleCondition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 60 | RL | 65.0 | 8450 | Pave | NaN | Reg | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | 7 | 5 | 2003 | 2003 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 196.0 | Gd | TA | PConc | Gd | TA | No | GLQ | 706 | Unf | 0 | 150 | 856 | GasA | Ex | Y | SBrkr | 856 | 854 | 0 | 1710 | 1 | 0 | 2 | 1 | 3 | 1 | Gd | 8 | Typ | 0 | NaN | Attchd | 2003.0 | RFn | 2 | 548 | TA | TA | Y | 0 | 61 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 2 | 2008 | WD | Normal |
| 1 | 20 | RL | 80.0 | 9600 | Pave | NaN | Reg | Lvl | AllPub | FR2 | Gtl | Veenker | Feedr | Norm | 1Fam | 1Story | 6 | 8 | 1976 | 1976 | Gable | CompShg | MetalSd | MetalSd | None | 0.0 | TA | TA | CBlock | Gd | TA | Gd | ALQ | 978 | Unf | 0 | 284 | 1262 | GasA | Ex | Y | SBrkr | 1262 | 0 | 0 | 1262 | 0 | 1 | 2 | 0 | 3 | 1 | TA | 6 | Typ | 1 | TA | Attchd | 1976.0 | RFn | 2 | 460 | TA | TA | Y | 298 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 5 | 2007 | WD | Normal |
| 2 | 60 | RL | 68.0 | 11250 | Pave | NaN | IR1 | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | 7 | 5 | 2001 | 2002 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 162.0 | Gd | TA | PConc | Gd | TA | Mn | GLQ | 486 | Unf | 0 | 434 | 920 | GasA | Ex | Y | SBrkr | 920 | 866 | 0 | 1786 | 1 | 0 | 2 | 1 | 3 | 1 | Gd | 6 | Typ | 1 | TA | Attchd | 2001.0 | RFn | 2 | 608 | TA | TA | Y | 0 | 42 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 9 | 2008 | WD | Normal |
| 3 | 70 | RL | 60.0 | 9550 | Pave | NaN | IR1 | Lvl | AllPub | Corner | Gtl | Crawfor | Norm | Norm | 1Fam | 2Story | 7 | 5 | 1915 | 1970 | Gable | CompShg | Wd Sdng | Wd Shng | None | 0.0 | TA | TA | BrkTil | TA | Gd | No | ALQ | 216 | Unf | 0 | 540 | 756 | GasA | Gd | Y | SBrkr | 961 | 756 | 0 | 1717 | 1 | 0 | 1 | 0 | 3 | 1 | Gd | 7 | Typ | 1 | Gd | Detchd | 1998.0 | Unf | 3 | 642 | TA | TA | Y | 0 | 35 | 272 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 2 | 2006 | WD | Abnorml |
| 4 | 60 | RL | 84.0 | 14260 | Pave | NaN | IR1 | Lvl | AllPub | FR2 | Gtl | NoRidge | Norm | Norm | 1Fam | 2Story | 8 | 5 | 2000 | 2000 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 350.0 | Gd | TA | PConc | Gd | TA | Av | GLQ | 655 | Unf | 0 | 490 | 1145 | GasA | Ex | Y | SBrkr | 1145 | 1053 | 0 | 2198 | 1 | 0 | 2 | 1 | 4 | 1 | Gd | 9 | Typ | 1 | TA | Attchd | 2000.0 | RFn | 3 | 836 | TA | TA | Y | 192 | 84 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 12 | 2008 | WD | Normal |
test1.head()
| MSSubClass | MSZoning | LotFrontage | LotArea | Street | Alley | LotShape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | Condition2 | BldgType | HouseStyle | OverallQual | OverallCond | YearBuilt | YearRemodAdd | RoofStyle | RoofMatl | Exterior1st | Exterior2nd | MasVnrType | MasVnrArea | ExterQual | ExterCond | Foundation | BsmtQual | BsmtCond | BsmtExposure | BsmtFinType1 | BsmtFinSF1 | BsmtFinType2 | BsmtFinSF2 | BsmtUnfSF | TotalBsmtSF | Heating | HeatingQC | CentralAir | Electrical | 1stFlrSF | 2ndFlrSF | LowQualFinSF | GrLivArea | BsmtFullBath | BsmtHalfBath | FullBath | HalfBath | BedroomAbvGr | KitchenAbvGr | KitchenQual | TotRmsAbvGrd | Functional | Fireplaces | FireplaceQu | GarageType | GarageYrBlt | GarageFinish | GarageCars | GarageArea | GarageQual | GarageCond | PavedDrive | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | PoolQC | Fence | MiscFeature | MiscVal | MoSold | YrSold | SaleType | SaleCondition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 20 | RH | 80.0 | 11622 | Pave | NaN | Reg | Lvl | AllPub | Inside | Gtl | NAmes | Feedr | Norm | 1Fam | 1Story | 5 | 6 | 1961 | 1961 | Gable | CompShg | VinylSd | VinylSd | None | 0.0 | TA | TA | CBlock | TA | TA | No | Rec | 468.0 | LwQ | 144.0 | 270.0 | 882.0 | GasA | TA | Y | SBrkr | 896 | 0 | 0 | 896 | 0.0 | 0.0 | 1 | 0 | 2 | 1 | TA | 5 | Typ | 0 | NaN | Attchd | 1961.0 | Unf | 1.0 | 730.0 | TA | TA | Y | 140 | 0 | 0 | 0 | 120 | 0 | NaN | MnPrv | NaN | 0 | 6 | 2010 | WD | Normal |
| 1 | 20 | RL | 81.0 | 14267 | Pave | NaN | IR1 | Lvl | AllPub | Corner | Gtl | NAmes | Norm | Norm | 1Fam | 1Story | 6 | 6 | 1958 | 1958 | Hip | CompShg | Wd Sdng | Wd Sdng | BrkFace | 108.0 | TA | TA | CBlock | TA | TA | No | ALQ | 923.0 | Unf | 0.0 | 406.0 | 1329.0 | GasA | TA | Y | SBrkr | 1329 | 0 | 0 | 1329 | 0.0 | 0.0 | 1 | 1 | 3 | 1 | Gd | 6 | Typ | 0 | NaN | Attchd | 1958.0 | Unf | 1.0 | 312.0 | TA | TA | Y | 393 | 36 | 0 | 0 | 0 | 0 | NaN | NaN | Gar2 | 12500 | 6 | 2010 | WD | Normal |
| 2 | 60 | RL | 74.0 | 13830 | Pave | NaN | IR1 | Lvl | AllPub | Inside | Gtl | Gilbert | Norm | Norm | 1Fam | 2Story | 5 | 5 | 1997 | 1998 | Gable | CompShg | VinylSd | VinylSd | None | 0.0 | TA | TA | PConc | Gd | TA | No | GLQ | 791.0 | Unf | 0.0 | 137.0 | 928.0 | GasA | Gd | Y | SBrkr | 928 | 701 | 0 | 1629 | 0.0 | 0.0 | 2 | 1 | 3 | 1 | TA | 6 | Typ | 1 | TA | Attchd | 1997.0 | Fin | 2.0 | 482.0 | TA | TA | Y | 212 | 34 | 0 | 0 | 0 | 0 | NaN | MnPrv | NaN | 0 | 3 | 2010 | WD | Normal |
| 3 | 60 | RL | 78.0 | 9978 | Pave | NaN | IR1 | Lvl | AllPub | Inside | Gtl | Gilbert | Norm | Norm | 1Fam | 2Story | 6 | 6 | 1998 | 1998 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 20.0 | TA | TA | PConc | TA | TA | No | GLQ | 602.0 | Unf | 0.0 | 324.0 | 926.0 | GasA | Ex | Y | SBrkr | 926 | 678 | 0 | 1604 | 0.0 | 0.0 | 2 | 1 | 3 | 1 | Gd | 7 | Typ | 1 | Gd | Attchd | 1998.0 | Fin | 2.0 | 470.0 | TA | TA | Y | 360 | 36 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 6 | 2010 | WD | Normal |
| 4 | 120 | RL | 43.0 | 5005 | Pave | NaN | IR1 | HLS | AllPub | Inside | Gtl | StoneBr | Norm | Norm | TwnhsE | 1Story | 8 | 5 | 1992 | 1992 | Gable | CompShg | HdBoard | HdBoard | None | 0.0 | Gd | TA | PConc | Gd | TA | No | ALQ | 263.0 | Unf | 0.0 | 1017.0 | 1280.0 | GasA | Ex | Y | SBrkr | 1280 | 0 | 0 | 1280 | 0.0 | 0.0 | 2 | 0 | 2 | 1 | Gd | 5 | Typ | 0 | NaN | Attchd | 1992.0 | RFn | 2.0 | 506.0 | TA | TA | Y | 0 | 82 | 0 | 0 | 144 | 0 | NaN | NaN | NaN | 0 | 1 | 2010 | WD | Normal |
That's a lot of columns. In simple words, we need to be selective of what features to include. Note: Read the data description carefully to understand the column features.
def summary_num(numerical_features):
""" Function to summarize all numeric features"""
print(f'There are {len(numerical_features)} observation in our dataset, {len(numerical_features[numerical_features.duplicated()])} of which are duplicated')
desc = numerical_features.describe().T
# hnzwd dlw2ty kam haga mn 3ndna
desc['nunique'] = numerical_features.nunique() # hya number of unique value tknow is it Continous feature or categoricalfeatures
desc['%unique'] = numerical_features.nunique() / len(numerical_features) * 100
desc['Null'] = numerical_features.isna().sum()
return desc
numerical_features = train1.select_dtypes(include=[np.number])
summary_num(numerical_features=numerical_features)
There are 1460 observation in our dataset, 1 of which are duplicated
| count | mean | std | min | 25% | 50% | 75% | max | nunique | %unique | Null | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| MSSubClass | 1460.0 | 56.897260 | 42.300571 | 20.0 | 20.00 | 50.0 | 70.00 | 190.0 | 15 | 1.027397 | 0 |
| LotFrontage | 1201.0 | 70.049958 | 24.284752 | 21.0 | 59.00 | 69.0 | 80.00 | 313.0 | 110 | 7.534247 | 259 |
| LotArea | 1460.0 | 10516.828082 | 9981.264932 | 1300.0 | 7553.50 | 9478.5 | 11601.50 | 215245.0 | 1073 | 73.493151 | 0 |
| OverallQual | 1460.0 | 6.099315 | 1.382997 | 1.0 | 5.00 | 6.0 | 7.00 | 10.0 | 10 | 0.684932 | 0 |
| OverallCond | 1460.0 | 5.575342 | 1.112799 | 1.0 | 5.00 | 5.0 | 6.00 | 9.0 | 9 | 0.616438 | 0 |
| YearBuilt | 1460.0 | 1971.267808 | 30.202904 | 1872.0 | 1954.00 | 1973.0 | 2000.00 | 2010.0 | 112 | 7.671233 | 0 |
| YearRemodAdd | 1460.0 | 1984.865753 | 20.645407 | 1950.0 | 1967.00 | 1994.0 | 2004.00 | 2010.0 | 61 | 4.178082 | 0 |
| MasVnrArea | 1452.0 | 103.685262 | 181.066207 | 0.0 | 0.00 | 0.0 | 166.00 | 1600.0 | 327 | 22.397260 | 8 |
| BsmtFinSF1 | 1460.0 | 443.639726 | 456.098091 | 0.0 | 0.00 | 383.5 | 712.25 | 5644.0 | 637 | 43.630137 | 0 |
| BsmtFinSF2 | 1460.0 | 46.549315 | 161.319273 | 0.0 | 0.00 | 0.0 | 0.00 | 1474.0 | 144 | 9.863014 | 0 |
| BsmtUnfSF | 1460.0 | 567.240411 | 441.866955 | 0.0 | 223.00 | 477.5 | 808.00 | 2336.0 | 780 | 53.424658 | 0 |
| TotalBsmtSF | 1460.0 | 1057.429452 | 438.705324 | 0.0 | 795.75 | 991.5 | 1298.25 | 6110.0 | 721 | 49.383562 | 0 |
| 1stFlrSF | 1460.0 | 1162.626712 | 386.587738 | 334.0 | 882.00 | 1087.0 | 1391.25 | 4692.0 | 753 | 51.575342 | 0 |
| 2ndFlrSF | 1460.0 | 346.992466 | 436.528436 | 0.0 | 0.00 | 0.0 | 728.00 | 2065.0 | 417 | 28.561644 | 0 |
| LowQualFinSF | 1460.0 | 5.844521 | 48.623081 | 0.0 | 0.00 | 0.0 | 0.00 | 572.0 | 24 | 1.643836 | 0 |
| GrLivArea | 1460.0 | 1515.463699 | 525.480383 | 334.0 | 1129.50 | 1464.0 | 1776.75 | 5642.0 | 861 | 58.972603 | 0 |
| BsmtFullBath | 1460.0 | 0.425342 | 0.518911 | 0.0 | 0.00 | 0.0 | 1.00 | 3.0 | 4 | 0.273973 | 0 |
| BsmtHalfBath | 1460.0 | 0.057534 | 0.238753 | 0.0 | 0.00 | 0.0 | 0.00 | 2.0 | 3 | 0.205479 | 0 |
| FullBath | 1460.0 | 1.565068 | 0.550916 | 0.0 | 1.00 | 2.0 | 2.00 | 3.0 | 4 | 0.273973 | 0 |
| HalfBath | 1460.0 | 0.382877 | 0.502885 | 0.0 | 0.00 | 0.0 | 1.00 | 2.0 | 3 | 0.205479 | 0 |
| BedroomAbvGr | 1460.0 | 2.866438 | 0.815778 | 0.0 | 2.00 | 3.0 | 3.00 | 8.0 | 8 | 0.547945 | 0 |
| KitchenAbvGr | 1460.0 | 1.046575 | 0.220338 | 0.0 | 1.00 | 1.0 | 1.00 | 3.0 | 4 | 0.273973 | 0 |
| TotRmsAbvGrd | 1460.0 | 6.517808 | 1.625393 | 2.0 | 5.00 | 6.0 | 7.00 | 14.0 | 12 | 0.821918 | 0 |
| Fireplaces | 1460.0 | 0.613014 | 0.644666 | 0.0 | 0.00 | 1.0 | 1.00 | 3.0 | 4 | 0.273973 | 0 |
| GarageYrBlt | 1379.0 | 1978.506164 | 24.689725 | 1900.0 | 1961.00 | 1980.0 | 2002.00 | 2010.0 | 97 | 6.643836 | 81 |
| GarageCars | 1460.0 | 1.767123 | 0.747315 | 0.0 | 1.00 | 2.0 | 2.00 | 4.0 | 5 | 0.342466 | 0 |
| GarageArea | 1460.0 | 472.980137 | 213.804841 | 0.0 | 334.50 | 480.0 | 576.00 | 1418.0 | 441 | 30.205479 | 0 |
| WoodDeckSF | 1460.0 | 94.244521 | 125.338794 | 0.0 | 0.00 | 0.0 | 168.00 | 857.0 | 274 | 18.767123 | 0 |
| OpenPorchSF | 1460.0 | 46.660274 | 66.256028 | 0.0 | 0.00 | 25.0 | 68.00 | 547.0 | 202 | 13.835616 | 0 |
| EnclosedPorch | 1460.0 | 21.954110 | 61.119149 | 0.0 | 0.00 | 0.0 | 0.00 | 552.0 | 120 | 8.219178 | 0 |
| 3SsnPorch | 1460.0 | 3.409589 | 29.317331 | 0.0 | 0.00 | 0.0 | 0.00 | 508.0 | 20 | 1.369863 | 0 |
| ScreenPorch | 1460.0 | 15.060959 | 55.757415 | 0.0 | 0.00 | 0.0 | 0.00 | 480.0 | 76 | 5.205479 | 0 |
| PoolArea | 1460.0 | 2.758904 | 40.177307 | 0.0 | 0.00 | 0.0 | 0.00 | 738.0 | 8 | 0.547945 | 0 |
| MiscVal | 1460.0 | 43.489041 | 496.123024 | 0.0 | 0.00 | 0.0 | 0.00 | 15500.0 | 21 | 1.438356 | 0 |
| MoSold | 1460.0 | 6.321918 | 2.703626 | 1.0 | 5.00 | 6.0 | 8.00 | 12.0 | 12 | 0.821918 | 0 |
| YrSold | 1460.0 | 2007.815753 | 1.328095 | 2006.0 | 2007.00 | 2008.0 | 2009.00 | 2010.0 | 5 | 0.342466 | 0 |
Insights from numerical features summary:
def summary_cat(categorical_features):
""" Function to summarize all Categorical features"""
print(f'There are {len(categorical_features)} observation in our dataset, {len(categorical_features[categorical_features.duplicated()])} of which are duplicated')
desc = categorical_features.describe().T
unique = categorical_features.nunique()
desc['%unique'] = unique / len(categorical_features) * 100
desc['Null'] = categorical_features.isna().sum()
return desc
categorical_features = train1.select_dtypes(include=['object'])
summary_cat(categorical_features=categorical_features)
There are 1460 observation in our dataset, 21 of which are duplicated
| count | unique | top | freq | %unique | Null | |
|---|---|---|---|---|---|---|
| MSZoning | 1460 | 5 | RL | 1151 | 0.342466 | 0 |
| Street | 1460 | 2 | Pave | 1454 | 0.136986 | 0 |
| Alley | 91 | 2 | Grvl | 50 | 0.136986 | 1369 |
| LotShape | 1460 | 4 | Reg | 925 | 0.273973 | 0 |
| LandContour | 1460 | 4 | Lvl | 1311 | 0.273973 | 0 |
| Utilities | 1460 | 2 | AllPub | 1459 | 0.136986 | 0 |
| LotConfig | 1460 | 5 | Inside | 1052 | 0.342466 | 0 |
| LandSlope | 1460 | 3 | Gtl | 1382 | 0.205479 | 0 |
| Neighborhood | 1460 | 25 | NAmes | 225 | 1.712329 | 0 |
| Condition1 | 1460 | 9 | Norm | 1260 | 0.616438 | 0 |
| Condition2 | 1460 | 8 | Norm | 1445 | 0.547945 | 0 |
| BldgType | 1460 | 5 | 1Fam | 1220 | 0.342466 | 0 |
| HouseStyle | 1460 | 8 | 1Story | 726 | 0.547945 | 0 |
| RoofStyle | 1460 | 6 | Gable | 1141 | 0.410959 | 0 |
| RoofMatl | 1460 | 8 | CompShg | 1434 | 0.547945 | 0 |
| Exterior1st | 1460 | 15 | VinylSd | 515 | 1.027397 | 0 |
| Exterior2nd | 1460 | 16 | VinylSd | 504 | 1.095890 | 0 |
| MasVnrType | 1452 | 4 | None | 864 | 0.273973 | 8 |
| ExterQual | 1460 | 4 | TA | 906 | 0.273973 | 0 |
| ExterCond | 1460 | 5 | TA | 1282 | 0.342466 | 0 |
| Foundation | 1460 | 6 | PConc | 647 | 0.410959 | 0 |
| BsmtQual | 1423 | 4 | TA | 649 | 0.273973 | 37 |
| BsmtCond | 1423 | 4 | TA | 1311 | 0.273973 | 37 |
| BsmtExposure | 1422 | 4 | No | 953 | 0.273973 | 38 |
| BsmtFinType1 | 1423 | 6 | Unf | 430 | 0.410959 | 37 |
| BsmtFinType2 | 1422 | 6 | Unf | 1256 | 0.410959 | 38 |
| Heating | 1460 | 6 | GasA | 1428 | 0.410959 | 0 |
| HeatingQC | 1460 | 5 | Ex | 741 | 0.342466 | 0 |
| CentralAir | 1460 | 2 | Y | 1365 | 0.136986 | 0 |
| Electrical | 1459 | 5 | SBrkr | 1334 | 0.342466 | 1 |
| KitchenQual | 1460 | 4 | TA | 735 | 0.273973 | 0 |
| Functional | 1460 | 7 | Typ | 1360 | 0.479452 | 0 |
| FireplaceQu | 770 | 5 | Gd | 380 | 0.342466 | 690 |
| GarageType | 1379 | 6 | Attchd | 870 | 0.410959 | 81 |
| GarageFinish | 1379 | 3 | Unf | 605 | 0.205479 | 81 |
| GarageQual | 1379 | 5 | TA | 1311 | 0.342466 | 81 |
| GarageCond | 1379 | 5 | TA | 1326 | 0.342466 | 81 |
| PavedDrive | 1460 | 3 | Y | 1340 | 0.205479 | 0 |
| PoolQC | 7 | 3 | Gd | 3 | 0.205479 | 1453 |
| Fence | 281 | 4 | MnPrv | 157 | 0.273973 | 1179 |
| MiscFeature | 54 | 4 | Shed | 49 | 0.273973 | 1406 |
| SaleType | 1460 | 9 | WD | 1267 | 0.616438 | 0 |
| SaleCondition | 1460 | 6 | Normal | 1198 | 0.410959 | 0 |
Insights from Categorical features summary:
Let's take a look on the target variable
target.describe()
count 1460.000000 mean 180921.195890 std 79442.502883 min 34900.000000 25% 129975.000000 50% 163000.000000 75% 214000.000000 max 755000.000000 Name: SalePrice, dtype: float64
plt.plot(figsize=(10,10), dpi=300)
sns.histplot(x=target, bins=30, kde=True)
plt.show()
# skewness
print("Skewness: %f" % target.skew())
Skewness: 1.882876
Let's take a look on Numerical data for following purposes:
# we have 36 numeric feature
numerical_features.shape
(1460, 36)
numerical_features.columns
Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',
'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',
'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF',
'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath',
'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces',
'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',
'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',
'MoSold', 'YrSold'],
dtype='object')
# tb3n a7na 3rfeen wshofna mn al description al foo2 an 3ndna fe hgat category gwa bs numeric f3yzeen n3rf hya eh wn7otha fe list
cat_feat = []
numerical_features.hist(figsize=(16, 20), bins=50, xlabelsize=8, ylabelsize=8);
MSSubClass feature: Identifies the type of dwelling involved in the sale.
numerical_features['MSSubClass'].unique()
array([ 60, 20, 70, 50, 190, 45, 90, 120, 30, 85, 80, 160, 75,
180, 40], dtype=int64)
cat_feat.append('MSSubClass')
sns.boxplot( x=numerical_features['MSSubClass'], y=target)
<AxesSubplot:xlabel='MSSubClass', ylabel='SalePrice'>
LotFrontage: Linear feet of street connected to property
plt.scatter(numerical_features['LotFrontage'], target)
plt.xlabel("LotFrontage")
plt.ylabel('SalePrice')
Text(0, 0.5, 'SalePrice')
LotArea: Lot size in square feet
plt.scatter(numerical_features['LotArea'], target)
plt.xlabel("LotArea")
plt.ylabel('SalePrice')
Text(0, 0.5, 'SalePrice')
OverallQual feature: Rates the overall material and finish of the house
sns.boxplot(x = numerical_features['OverallQual'], y = target)
cat_feat.append('OverallQual')
OverallCond: Rates the overall condition of the house
sns.boxplot(x=numerical_features['OverallCond'], y=target)
cat_feat.append('OverallCond')
YearBuilt: Original construction date
plt.scatter(numerical_features['YearBuilt'], target)
plt.xlabel('YearBuilt')
plt.ylabel('SalePrice')
cat_feat.append('YearBuilt')
YearRemodAdd: Remodel date (same as construction date if no remodeling or additions)
plt.scatter(numerical_features['YearRemodAdd'], target)
plt.xlabel('YearRemodAdd')
plt.ylabel('SalePrice')
cat_feat.append('YearRemodAdd')
MasVnrArea: Masonry veneer area in square feet
plt.scatter(numerical_features['MasVnrArea'], target)
plt.xlabel('MasVnrArea')
plt.ylabel('SalePrice')
Text(0, 0.5, 'SalePrice')
TotalBsmtSF: Total square feet of basement area
plt.scatter(numerical_features['TotalBsmtSF'], target)
plt.xlabel('TotalBxmtSF')
plt.ylabel('SalePrice')
Text(0, 0.5, 'SalePrice')
1stFlrSF: First Floor square feet 2ndFlrSF: Second floor square feet
plt.scatter(numerical_features['1stFlrSF'],target)
plt.xlabel('1stFlrSFF')
plt.ylabel('SalePrice')
plt.show()
plt.scatter(numerical_features['2ndFlrSF'],target)
plt.xlabel('2ndFlrSFF')
plt.ylabel('SalePrice')
plt.show()
LowQualFinSF: Low quality finished square feet (all floors)
GrLivArea: Above grade (ground) living area square feet
plt.scatter(numerical_features['GrLivArea'],target)
plt.xlabel('GrLivArea')
plt.ylabel('SalePrice')
plt.show()
BsmtFullBath: Basement full bathrooms
FullBath: Full bathrooms above grade
HalfBath: Half baths above grade
Bedroom: Bedrooms above grade (does NOT include basement bedrooms)
Kitchen: Kitchens above grade
Fireplaces: Number of fireplaces
for feature in ['FullBath','HalfBath','BedroomAbvGr','KitchenAbvGr','Fireplaces']:
sns.boxplot(data = numerical_features,x=feature,y=target)
plt.show()
cat_feat.append(feature)
GarageYrBlt: Year garage was built
plt.scatter(numerical_features['GarageYrBlt'],target)
plt.xlabel('GarageYrBlt')
plt.ylabel('SalePrice')
plt.show()
GarageCars: Size of garage in car capacity
sns.boxplot(x=numerical_features['GarageCars'],y= target)
plt.show()
cat_feat.append('GarageCars')
GarageArea: Size of garage in square feet
plt.scatter(numerical_features['GarageArea'],target)
plt.xlabel('GarageArea')
plt.ylabel('SalePrice')
plt.show()
for feature in ['WoodDeckSF','OpenPorchSF','EnclosedPorch','3SsnPorch','ScreenPorch','PoolArea']:
plt.scatter(numerical_features[feature],target)
plt.xlabel(feature)
plt.ylabel('SalePrice')
plt.show()
# cat_feat.append(feature)
MoSold: Month House was sold in
sns.lineplot(x=numerical_features['MoSold'], y=target)
cat_feat.append('MoSold')
YrSold: Year Sold (YYYY)
sns.lineplot(x=numerical_features['YrSold'], y=target)
cat_feat.append('YrSold')
def heatmap(df,label=None):
plt.figure(figsize = (16,10),dpi=300)
sns.heatmap(df.corr(method='pearson'),cmap='YlOrRd',annot=True,annot_kws={'size':7})
plt.title(f'Correlations between features of the {label} set')
plt.show()
heatmap(numerical_features)
Feature enginnering ideas about numeric features inspired by analysis:
categorical_features.head()
| MSZoning | Street | Alley | LotShape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | Condition2 | BldgType | HouseStyle | RoofStyle | RoofMatl | Exterior1st | Exterior2nd | MasVnrType | ExterQual | ExterCond | Foundation | BsmtQual | BsmtCond | BsmtExposure | BsmtFinType1 | BsmtFinType2 | Heating | HeatingQC | CentralAir | Electrical | KitchenQual | Functional | FireplaceQu | GarageType | GarageFinish | GarageQual | GarageCond | PavedDrive | PoolQC | Fence | MiscFeature | SaleType | SaleCondition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | RL | Pave | NaN | Reg | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | No | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | NaN | Attchd | RFn | TA | TA | Y | NaN | NaN | NaN | WD | Normal |
| 1 | RL | Pave | NaN | Reg | Lvl | AllPub | FR2 | Gtl | Veenker | Feedr | Norm | 1Fam | 1Story | Gable | CompShg | MetalSd | MetalSd | None | TA | TA | CBlock | Gd | TA | Gd | ALQ | Unf | GasA | Ex | Y | SBrkr | TA | Typ | TA | Attchd | RFn | TA | TA | Y | NaN | NaN | NaN | WD | Normal |
| 2 | RL | Pave | NaN | IR1 | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | Mn | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | TA | Attchd | RFn | TA | TA | Y | NaN | NaN | NaN | WD | Normal |
| 3 | RL | Pave | NaN | IR1 | Lvl | AllPub | Corner | Gtl | Crawfor | Norm | Norm | 1Fam | 2Story | Gable | CompShg | Wd Sdng | Wd Shng | None | TA | TA | BrkTil | TA | Gd | No | ALQ | Unf | GasA | Gd | Y | SBrkr | Gd | Typ | Gd | Detchd | Unf | TA | TA | Y | NaN | NaN | NaN | WD | Abnorml |
| 4 | RL | Pave | NaN | IR1 | Lvl | AllPub | FR2 | Gtl | NoRidge | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | Av | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | TA | Attchd | RFn | TA | TA | Y | NaN | NaN | NaN | WD | Normal |
categorical_features['SalePrice'] = target
def category_analyze(feature, target):
sns.boxplot(data = categorical_features, x=feature, y=target)
plt.show()
analyze = pd.DataFrame(data = categorical_features.groupby(feature)[target].agg(['median','mean','count']))
"""
how analyze dataframe is created
1.'categorical_features.groupby(feature)': This groups the data in the "categorical_features" DataFrame by a column specified as "feature".
It groups the data into separate groups based on unique values in the "feature" column.
2. '[target]': This selects the column named "target" from the grouped data.
3 '.agg(['median', 'mean', 'count'])': This applies aggregation functions to the "target" column within each group.
It calculates the median, mean, and count for each group.
4. 'pd.DataFrame(data=...)': This creates a new DataFrame using the aggregated data. The "data" parameter is set to the result
of the aggregation performed in the previous step.
5. analyze = ...: This assigns the newly created DataFrame to the variable "analyze".
"""
return analyze
MSZoning : Identifies the general zoning classification of the sale.
category_analyze('MSZoning', 'SalePrice')
| median | mean | count | |
|---|---|---|---|
| MSZoning | |||
| C (all) | 74700.0 | 74528.000000 | 10 |
| FV | 205950.0 | 214014.061538 | 65 |
| RH | 136500.0 | 131558.375000 | 16 |
| RL | 174000.0 | 191004.994787 | 1151 |
| RM | 120500.0 | 126316.830275 | 218 |
Street: Type of road access to property
category_analyze('Street','SalePrice')
| median | mean | count | |
|---|---|---|---|
| Street | |||
| Grvl | 114250.0 | 130190.500000 | 6 |
| Pave | 163000.0 | 181130.538514 | 1454 |
Alley: Type of alley access to property
category_analyze('Alley','SalePrice')
| median | mean | count | |
|---|---|---|---|
| Alley | |||
| Grvl | 119500.0 | 122219.080000 | 50 |
| Pave | 172500.0 | 168000.585366 | 41 |
LotShape: General shape of property
category_analyze('LotShape','SalePrice')
| median | mean | count | |
|---|---|---|---|
| LotShape | |||
| IR1 | 189000.0 | 206101.665289 | 484 |
| IR2 | 221000.0 | 239833.365854 | 41 |
| IR3 | 203570.0 | 216036.500000 | 10 |
| Reg | 146000.0 | 164754.818378 | 925 |
LandContour: Flatness of the property
category_analyze('LandContour','SalePrice')
| median | mean | count | |
|---|---|---|---|
| LandContour | |||
| Bnk | 139400.0 | 143104.079365 | 63 |
| HLS | 222250.0 | 231533.940000 | 50 |
| Low | 190000.0 | 203661.111111 | 36 |
| Lvl | 162900.0 | 180183.746758 | 1311 |
LotConfig: Lot configuration
category_analyze('LotConfig', 'SalePrice')
| median | mean | count | |
|---|---|---|---|
| LotConfig | |||
| Corner | 160000.0 | 181623.425856 | 263 |
| CulDSac | 199262.0 | 223854.617021 | 94 |
| FR2 | 165000.0 | 177934.574468 | 47 |
| FR3 | 195450.0 | 208475.000000 | 4 |
| Inside | 159697.5 | 176938.047529 | 1052 |
LandSlope: Slope of property
category_analyze('LandSlope', 'SalePrice')
| median | mean | count | |
|---|---|---|---|
| LandSlope | |||
| Gtl | 161875.0 | 179956.799566 | 1382 |
| Mod | 186700.0 | 196734.138462 | 65 |
| Sev | 185000.0 | 204379.230769 | 13 |
Neighborhood: Physical locations within Ames city limits
category_analyze('Neighborhood', 'SalePrice')
# m3lsh msh 2adr agher al format bta3 alklam alt7t wakhleeh vertical y3ny 3shan ana 3aml function 3shan akhls
| median | mean | count | |
|---|---|---|---|
| Neighborhood | |||
| Blmngtn | 191000.0 | 194870.882353 | 17 |
| Blueste | 137500.0 | 137500.000000 | 2 |
| BrDale | 106000.0 | 104493.750000 | 16 |
| BrkSide | 124300.0 | 124834.051724 | 58 |
| ClearCr | 200250.0 | 212565.428571 | 28 |
| CollgCr | 197200.0 | 197965.773333 | 150 |
| Crawfor | 200624.0 | 210624.725490 | 51 |
| Edwards | 121750.0 | 128219.700000 | 100 |
| Gilbert | 181000.0 | 192854.506329 | 79 |
| IDOTRR | 103000.0 | 100123.783784 | 37 |
| MeadowV | 88000.0 | 98576.470588 | 17 |
| Mitchel | 153500.0 | 156270.122449 | 49 |
| NAmes | 140000.0 | 145847.080000 | 225 |
| NPkVill | 146000.0 | 142694.444444 | 9 |
| NWAmes | 182900.0 | 189050.068493 | 73 |
| NoRidge | 301500.0 | 335295.317073 | 41 |
| NridgHt | 315000.0 | 316270.623377 | 77 |
| OldTown | 119000.0 | 128225.300885 | 113 |
| SWISU | 139500.0 | 142591.360000 | 25 |
| Sawyer | 135000.0 | 136793.135135 | 74 |
| SawyerW | 179900.0 | 186555.796610 | 59 |
| Somerst | 225500.0 | 225379.837209 | 86 |
| StoneBr | 278000.0 | 310499.000000 | 25 |
| Timber | 228475.0 | 242247.447368 | 38 |
| Veenker | 218000.0 | 238772.727273 | 11 |
Condition1: Proximity to various conditions
category_analyze('Condition1','SalePrice')
| median | mean | count | |
|---|---|---|---|
| Condition1 | |||
| Artery | 119550.0 | 135091.666667 | 48 |
| Feedr | 140000.0 | 142475.481481 | 81 |
| Norm | 166500.0 | 184495.492063 | 1260 |
| PosA | 212500.0 | 225875.000000 | 8 |
| PosN | 200000.0 | 215184.210526 | 19 |
| RRAe | 142500.0 | 138400.000000 | 11 |
| RRAn | 171495.0 | 184396.615385 | 26 |
| RRNe | 190750.0 | 190750.000000 | 2 |
| RRNn | 214000.0 | 212400.000000 | 5 |
Condition2: Proximity to various conditions (if more than one is present)
category_analyze('Condition2','SalePrice')
| median | mean | count | |
|---|---|---|---|
| Condition2 | |||
| Artery | 106500.0 | 106500.000000 | 2 |
| Feedr | 127500.0 | 121166.666667 | 6 |
| Norm | 163500.0 | 181169.405536 | 1445 |
| PosA | 325000.0 | 325000.000000 | 1 |
| PosN | 284875.0 | 284875.000000 | 2 |
| RRAe | 190000.0 | 190000.000000 | 1 |
| RRAn | 136905.0 | 136905.000000 | 1 |
| RRNn | 96750.0 | 96750.000000 | 2 |
BldgType: Type of dwelling
category_analyze('BldgType','SalePrice')
| median | mean | count | |
|---|---|---|---|
| BldgType | |||
| 1Fam | 167900.0 | 185763.807377 | 1220 |
| 2fmCon | 127500.0 | 128432.258065 | 31 |
| Duplex | 135980.0 | 133541.076923 | 52 |
| Twnhs | 137500.0 | 135911.627907 | 43 |
| TwnhsE | 172200.0 | 181959.342105 | 114 |
HouseStyle: Style of dwelling
category_analyze('HouseStyle','SalePrice')
| median | mean | count | |
|---|---|---|---|
| HouseStyle | |||
| 1.5Fin | 132000.0 | 143116.740260 | 154 |
| 1.5Unf | 111250.0 | 110150.000000 | 14 |
| 1Story | 154750.0 | 175985.477961 | 726 |
| 2.5Fin | 194000.0 | 220000.000000 | 8 |
| 2.5Unf | 133900.0 | 157354.545455 | 11 |
| 2Story | 190000.0 | 210051.764045 | 445 |
| SFoyer | 135960.0 | 135074.486486 | 37 |
| SLvl | 164500.0 | 166703.384615 | 65 |
RoofStyle: Type of roof
category_analyze('RoofStyle','SalePrice')
| median | mean | count | |
|---|---|---|---|
| RoofStyle | |||
| Flat | 185000.0 | 194690.000000 | 13 |
| Gable | 160000.0 | 171483.956179 | 1141 |
| Gambrel | 139000.0 | 148909.090909 | 11 |
| Hip | 176500.0 | 218876.933566 | 286 |
| Mansard | 175000.0 | 180568.428571 | 7 |
| Shed | 225000.0 | 225000.000000 | 2 |
RoofMatl: Roof material
category_analyze('RoofMatl','SalePrice')
| median | mean | count | |
|---|---|---|---|
| RoofMatl | |||
| ClyTile | 160000.0 | 160000.000000 | 1 |
| CompShg | 162000.0 | 179803.679219 | 1434 |
| Membran | 241500.0 | 241500.000000 | 1 |
| Metal | 180000.0 | 180000.000000 | 1 |
| Roll | 137000.0 | 137000.000000 | 1 |
| Tar&Grv | 167000.0 | 185406.363636 | 11 |
| WdShake | 242000.0 | 241400.000000 | 5 |
| WdShngl | 332500.0 | 390250.000000 | 6 |
Exterior1st: Exterior covering on house
category_analyze('Exterior1st','SalePrice')
| median | mean | count | |
|---|---|---|---|
| Exterior1st | |||
| AsbShng | 108000.0 | 107385.550000 | 20 |
| AsphShn | 100000.0 | 100000.000000 | 1 |
| BrkComm | 71000.0 | 71000.000000 | 2 |
| BrkFace | 165750.0 | 194573.000000 | 50 |
| CBlock | 105000.0 | 105000.000000 | 1 |
| CemntBd | 236500.0 | 231690.655738 | 61 |
| HdBoard | 149900.0 | 163077.450450 | 222 |
| ImStucc | 262000.0 | 262000.000000 | 1 |
| MetalSd | 139000.0 | 149422.177273 | 220 |
| Plywood | 167450.0 | 175942.379630 | 108 |
| Stone | 258500.0 | 258500.000000 | 2 |
| Stucco | 144000.0 | 162990.000000 | 25 |
| VinylSd | 200000.0 | 213732.900971 | 515 |
| Wd Sdng | 138943.5 | 149841.645631 | 206 |
| WdShing | 128700.0 | 150655.076923 | 26 |
Exterior2nd: Exterior covering on house (if more than one material)
category_analyze('Exterior2nd','SalePrice')
| median | mean | count | |
|---|---|---|---|
| Exterior2nd | |||
| AsbShng | 111000.0 | 114060.550000 | 20 |
| AsphShn | 139000.0 | 138000.000000 | 3 |
| Brk Cmn | 147000.0 | 126714.285714 | 7 |
| BrkFace | 160000.0 | 195818.000000 | 25 |
| CBlock | 105000.0 | 105000.000000 | 1 |
| CmentBd | 238750.0 | 230093.833333 | 60 |
| HdBoard | 155000.0 | 167661.565217 | 207 |
| ImStucc | 187600.0 | 252070.000000 | 10 |
| MetalSd | 138750.0 | 149803.172897 | 214 |
| Other | 319000.0 | 319000.000000 | 1 |
| Plywood | 160750.0 | 168112.387324 | 142 |
| Stone | 177000.0 | 158224.800000 | 5 |
| Stucco | 142000.0 | 155905.153846 | 26 |
| VinylSd | 200070.5 | 214432.460317 | 504 |
| Wd Sdng | 138000.0 | 148386.065990 | 197 |
| Wd Shng | 138225.0 | 161328.947368 | 38 |
MasVnrType: Masonry veneer type
category_analyze('MasVnrType','SalePrice')
| median | mean | count | |
|---|---|---|---|
| MasVnrType | |||
| BrkCmn | 139000.0 | 146318.066667 | 15 |
| BrkFace | 181000.0 | 204691.871910 | 445 |
| None | 143000.0 | 156221.891204 | 864 |
| Stone | 246839.0 | 265583.625000 | 128 |
ExterQual: Evaluates the quality of the material on the exterior
category_analyze('ExterQual','SalePrice')
| median | mean | count | |
|---|---|---|---|
| ExterQual | |||
| Ex | 364606.5 | 367360.961538 | 52 |
| Fa | 82250.0 | 87985.214286 | 14 |
| Gd | 220000.0 | 231633.510246 | 488 |
| TA | 139450.0 | 144341.313466 | 906 |
ExterCond: Evaluates the present condition of the material on the exterior
category_analyze('ExterCond','SalePrice')
| median | mean | count | |
|---|---|---|---|
| ExterCond | |||
| Ex | 161000.0 | 201333.333333 | 3 |
| Fa | 95750.0 | 102595.142857 | 28 |
| Gd | 151250.0 | 168897.568493 | 146 |
| Po | 76500.0 | 76500.000000 | 1 |
| TA | 167370.0 | 184034.896256 | 1282 |
Foundation: Type of foundation
category_analyze('Foundation','SalePrice')
| median | mean | count | |
|---|---|---|---|
| Foundation | |||
| BrkTil | 125250.0 | 132291.075342 | 146 |
| CBlock | 141500.0 | 149805.714511 | 634 |
| PConc | 205000.0 | 225230.442040 | 647 |
| Slab | 104150.0 | 107365.625000 | 24 |
| Stone | 126500.0 | 165959.166667 | 6 |
| Wood | 164000.0 | 185666.666667 | 3 |
BsmtQual: Evaluates the height of the basement
category_analyze('BsmtQual','SalePrice')
| median | mean | count | |
|---|---|---|---|
| BsmtQual | |||
| Ex | 318000.0 | 327041.041322 | 121 |
| Fa | 112000.0 | 115692.028571 | 35 |
| Gd | 192070.0 | 202688.478964 | 618 |
| TA | 135500.0 | 140759.818182 | 649 |
BsmtCond: Evaluates the general condition of the basement
category_analyze('BsmtCond','SalePrice')
| median | mean | count | |
|---|---|---|---|
| BsmtCond | |||
| Fa | 118500.0 | 121809.533333 | 45 |
| Gd | 193879.0 | 213599.907692 | 65 |
| Po | 64000.0 | 64000.000000 | 2 |
| TA | 165000.0 | 183632.620900 | 1311 |
BsmtExposure: Refers to walkout or garden level walls
category_analyze('BsmtExposure','SalePrice')
| median | mean | count | |
|---|---|---|---|
| BsmtExposure | |||
| Av | 185850.0 | 206643.420814 | 221 |
| Gd | 226975.0 | 257689.805970 | 134 |
| Mn | 182450.0 | 192789.657895 | 114 |
| No | 154000.0 | 165652.295908 | 953 |
Heating: Type of heating
category_analyze('Heating','SalePrice')
| median | mean | count | |
|---|---|---|---|
| Heating | |||
| Floor | 72500.0 | 72500.000000 | 1 |
| GasA | 164500.0 | 182021.195378 | 1428 |
| GasW | 134950.0 | 166632.166667 | 18 |
| Grav | 79000.0 | 75271.428571 | 7 |
| OthW | 125750.0 | 125750.000000 | 2 |
| Wall | 91450.0 | 92100.000000 | 4 |
HeatingQC: Heating quality and condition
category_analyze('HeatingQC','SalePrice')
| median | mean | count | |
|---|---|---|---|
| HeatingQC | |||
| Ex | 194700.0 | 214914.429150 | 741 |
| Fa | 123500.0 | 123919.489796 | 49 |
| Gd | 152000.0 | 156858.871369 | 241 |
| Po | 87000.0 | 87000.000000 | 1 |
| TA | 135000.0 | 142362.876168 | 428 |
CentralAir: Central air conditioning
category_analyze('CentralAir','SalePrice')
| median | mean | count | |
|---|---|---|---|
| CentralAir | |||
| N | 98000.0 | 105264.073684 | 95 |
| Y | 168000.0 | 186186.709890 | 1365 |
Electrical: Electrical system
category_analyze('Electrical','SalePrice')
| median | mean | count | |
|---|---|---|---|
| Electrical | |||
| FuseA | 121250.0 | 122196.893617 | 94 |
| FuseF | 115000.0 | 107675.444444 | 27 |
| FuseP | 82000.0 | 97333.333333 | 3 |
| Mix | 67000.0 | 67000.000000 | 1 |
| SBrkr | 170000.0 | 186825.113193 | 1334 |
KitchenQual: Kitchen quality
category_analyze('KitchenQual','SalePrice')
| median | mean | count | |
|---|---|---|---|
| KitchenQual | |||
| Ex | 316750.0 | 328554.670000 | 100 |
| Fa | 115000.0 | 105565.205128 | 39 |
| Gd | 201400.0 | 212116.023891 | 586 |
| TA | 137000.0 | 139962.511565 | 735 |
Functional: Home functionality (Assume typical unless deductions are warranted)
category_analyze('Functional','SalePrice')
| median | mean | count | |
|---|---|---|---|
| Functional | |||
| Maj1 | 140750.0 | 153948.142857 | 14 |
| Maj2 | 85000.0 | 85800.000000 | 5 |
| Min1 | 139000.0 | 146385.483871 | 31 |
| Min2 | 140000.0 | 144240.647059 | 34 |
| Mod | 137900.0 | 168393.333333 | 15 |
| Sev | 129000.0 | 129000.000000 | 1 |
| Typ | 165500.0 | 183429.147059 | 1360 |
FireplaceQu: Fireplace quality
category_analyze('FireplaceQu','SalePrice')
| median | mean | count | |
|---|---|---|---|
| FireplaceQu | |||
| Ex | 314250.0 | 337712.500000 | 24 |
| Fa | 158000.0 | 167298.484848 | 33 |
| Gd | 206950.0 | 226351.415789 | 380 |
| Po | 131500.0 | 129764.150000 | 20 |
| TA | 187500.0 | 205723.488818 | 313 |
GarageType: Garage location
category_analyze('GarageType','SalePrice')
| median | mean | count | |
|---|---|---|---|
| GarageType | |||
| 2Types | 159000.0 | 151283.333333 | 6 |
| Attchd | 185000.0 | 202892.656322 | 870 |
| Basment | 148000.0 | 160570.684211 | 19 |
| BuiltIn | 227500.0 | 254751.738636 | 88 |
| CarPort | 108000.0 | 109962.111111 | 9 |
| Detchd | 129500.0 | 134091.162791 | 387 |
GarageFinish: Interior finish of the garage
category_analyze('GarageFinish','SalePrice')
| median | mean | count | |
|---|---|---|---|
| GarageFinish | |||
| Fin | 215000.0 | 240052.690341 | 352 |
| RFn | 190000.0 | 202068.869668 | 422 |
| Unf | 135000.0 | 142156.423140 | 605 |
GarageQual: Garage quality
category_analyze('GarageQual','SalePrice')
| median | mean | count | |
|---|---|---|---|
| GarageQual | |||
| Ex | 127500.0 | 241000.000000 | 3 |
| Fa | 115000.0 | 123573.354167 | 48 |
| Gd | 209115.0 | 215860.714286 | 14 |
| Po | 96500.0 | 100166.666667 | 3 |
| TA | 170000.0 | 187489.836003 | 1311 |
GarageCond: Garage condition
category_analyze('GarageCond','SalePrice')
| median | mean | count | |
|---|---|---|---|
| GarageCond | |||
| Ex | 124000.0 | 124000.000000 | 2 |
| Fa | 114504.0 | 114654.028571 | 35 |
| Gd | 148000.0 | 179930.000000 | 9 |
| Po | 108000.0 | 108500.000000 | 7 |
| TA | 170000.0 | 187885.735294 | 1326 |
PavedDrive: Paved driveway
category_analyze('PavedDrive','SalePrice')
| median | mean | count | |
|---|---|---|---|
| PavedDrive | |||
| N | 111000.0 | 115039.122222 | 90 |
| P | 132250.0 | 132330.000000 | 30 |
| Y | 168500.0 | 186433.973881 | 1340 |
PoolQC: Pool quality
category_analyze('PoolQC','SalePrice')
| median | mean | count | |
|---|---|---|---|
| PoolQC | |||
| Ex | 490000.0 | 490000.0 | 2 |
| Fa | 215500.0 | 215500.0 | 2 |
| Gd | 171000.0 | 201990.0 | 3 |
Fence: Fence quality
category_analyze('Fence','SalePrice')
| median | mean | count | |
|---|---|---|---|
| Fence | |||
| GdPrv | 167500.0 | 178927.457627 | 59 |
| GdWo | 138750.0 | 140379.314815 | 54 |
| MnPrv | 137450.0 | 148751.089172 | 157 |
| MnWw | 130000.0 | 134286.363636 | 11 |
MiscFeature: Miscellaneous feature not covered in other categories
category_analyze('MiscFeature','SalePrice')
| median | mean | count | |
|---|---|---|---|
| MiscFeature | |||
| Gar2 | 170750.0 | 170750.000000 | 2 |
| Othr | 94000.0 | 94000.000000 | 2 |
| Shed | 144000.0 | 151187.612245 | 49 |
| TenC | 250000.0 | 250000.000000 | 1 |
SaleType: Type of sale
category_analyze('SaleType','SalePrice')
| median | mean | count | |
|---|---|---|---|
| SaleType | |||
| COD | 139000.0 | 143973.255814 | 43 |
| CWD | 188750.0 | 210600.000000 | 4 |
| Con | 269600.0 | 269600.000000 | 2 |
| ConLD | 140000.0 | 138780.888889 | 9 |
| ConLI | 125000.0 | 200390.000000 | 5 |
| ConLw | 144000.0 | 143700.000000 | 5 |
| New | 247453.0 | 274945.418033 | 122 |
| Oth | 116050.0 | 119850.000000 | 3 |
| WD | 158000.0 | 173401.836622 | 1267 |
SaleCondition: Condition of sale
category_analyze('SaleCondition','SalePrice')
| median | mean | count | |
|---|---|---|---|
| SaleCondition | |||
| Abnorml | 130000.0 | 146526.623762 | 101 |
| AdjLand | 104000.0 | 104125.000000 | 4 |
| Alloca | 148145.0 | 167377.416667 | 12 |
| Family | 140500.0 | 149600.000000 | 20 |
| Normal | 160000.0 | 175202.219533 | 1198 |
| Partial | 244600.0 | 272291.752000 | 125 |
categorical_features.columns
Index(['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities',
'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',
'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st',
'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation',
'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',
'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',
'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual',
'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature',
'SaleType', 'SaleCondition', 'SalePrice'],
dtype='object')
Feature engineering ideas about categorical features inspired from analysis:
target = train0['SalePrice'] # ana 3yzo m3aya f ana hkhzno dlw2ty 3shan h3mlo drop
test_ids = test0['Id'] # wda ana 3yzo brdo f khzno m3aya 3shan al sample sumbmissions
train1 = train0.drop(['Id', 'SalePrice'], axis=1)
test1 = test0.drop('Id', axis=1)
# tb hwa ana leeh a3ml concat, hyzwd al accuracy bt3ty wna bfill al missing data
data1 = pd.concat([train1,test1], axis=0).reset_index(drop=True)
data1
| MSSubClass | MSZoning | LotFrontage | LotArea | Street | Alley | LotShape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | Condition2 | BldgType | HouseStyle | OverallQual | OverallCond | YearBuilt | YearRemodAdd | RoofStyle | RoofMatl | Exterior1st | Exterior2nd | MasVnrType | MasVnrArea | ExterQual | ExterCond | Foundation | BsmtQual | BsmtCond | BsmtExposure | BsmtFinType1 | BsmtFinSF1 | BsmtFinType2 | BsmtFinSF2 | BsmtUnfSF | TotalBsmtSF | Heating | HeatingQC | CentralAir | Electrical | 1stFlrSF | 2ndFlrSF | LowQualFinSF | GrLivArea | BsmtFullBath | BsmtHalfBath | FullBath | HalfBath | BedroomAbvGr | KitchenAbvGr | KitchenQual | TotRmsAbvGrd | Functional | Fireplaces | FireplaceQu | GarageType | GarageYrBlt | GarageFinish | GarageCars | GarageArea | GarageQual | GarageCond | PavedDrive | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | PoolQC | Fence | MiscFeature | MiscVal | MoSold | YrSold | SaleType | SaleCondition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 60 | RL | 65.0 | 8450 | Pave | NaN | Reg | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | 7 | 5 | 2003 | 2003 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 196.0 | Gd | TA | PConc | Gd | TA | No | GLQ | 706.0 | Unf | 0.0 | 150.0 | 856.0 | GasA | Ex | Y | SBrkr | 856 | 854 | 0 | 1710 | 1.0 | 0.0 | 2 | 1 | 3 | 1 | Gd | 8 | Typ | 0 | NaN | Attchd | 2003.0 | RFn | 2.0 | 548.0 | TA | TA | Y | 0 | 61 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 2 | 2008 | WD | Normal |
| 1 | 20 | RL | 80.0 | 9600 | Pave | NaN | Reg | Lvl | AllPub | FR2 | Gtl | Veenker | Feedr | Norm | 1Fam | 1Story | 6 | 8 | 1976 | 1976 | Gable | CompShg | MetalSd | MetalSd | None | 0.0 | TA | TA | CBlock | Gd | TA | Gd | ALQ | 978.0 | Unf | 0.0 | 284.0 | 1262.0 | GasA | Ex | Y | SBrkr | 1262 | 0 | 0 | 1262 | 0.0 | 1.0 | 2 | 0 | 3 | 1 | TA | 6 | Typ | 1 | TA | Attchd | 1976.0 | RFn | 2.0 | 460.0 | TA | TA | Y | 298 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 5 | 2007 | WD | Normal |
| 2 | 60 | RL | 68.0 | 11250 | Pave | NaN | IR1 | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | 7 | 5 | 2001 | 2002 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 162.0 | Gd | TA | PConc | Gd | TA | Mn | GLQ | 486.0 | Unf | 0.0 | 434.0 | 920.0 | GasA | Ex | Y | SBrkr | 920 | 866 | 0 | 1786 | 1.0 | 0.0 | 2 | 1 | 3 | 1 | Gd | 6 | Typ | 1 | TA | Attchd | 2001.0 | RFn | 2.0 | 608.0 | TA | TA | Y | 0 | 42 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 9 | 2008 | WD | Normal |
| 3 | 70 | RL | 60.0 | 9550 | Pave | NaN | IR1 | Lvl | AllPub | Corner | Gtl | Crawfor | Norm | Norm | 1Fam | 2Story | 7 | 5 | 1915 | 1970 | Gable | CompShg | Wd Sdng | Wd Shng | None | 0.0 | TA | TA | BrkTil | TA | Gd | No | ALQ | 216.0 | Unf | 0.0 | 540.0 | 756.0 | GasA | Gd | Y | SBrkr | 961 | 756 | 0 | 1717 | 1.0 | 0.0 | 1 | 0 | 3 | 1 | Gd | 7 | Typ | 1 | Gd | Detchd | 1998.0 | Unf | 3.0 | 642.0 | TA | TA | Y | 0 | 35 | 272 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 2 | 2006 | WD | Abnorml |
| 4 | 60 | RL | 84.0 | 14260 | Pave | NaN | IR1 | Lvl | AllPub | FR2 | Gtl | NoRidge | Norm | Norm | 1Fam | 2Story | 8 | 5 | 2000 | 2000 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 350.0 | Gd | TA | PConc | Gd | TA | Av | GLQ | 655.0 | Unf | 0.0 | 490.0 | 1145.0 | GasA | Ex | Y | SBrkr | 1145 | 1053 | 0 | 2198 | 1.0 | 0.0 | 2 | 1 | 4 | 1 | Gd | 9 | Typ | 1 | TA | Attchd | 2000.0 | RFn | 3.0 | 836.0 | TA | TA | Y | 192 | 84 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 12 | 2008 | WD | Normal |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2914 | 160 | RM | 21.0 | 1936 | Pave | NaN | Reg | Lvl | AllPub | Inside | Gtl | MeadowV | Norm | Norm | Twnhs | 2Story | 4 | 7 | 1970 | 1970 | Gable | CompShg | CemntBd | CmentBd | None | 0.0 | TA | TA | CBlock | TA | TA | No | Unf | 0.0 | Unf | 0.0 | 546.0 | 546.0 | GasA | Gd | Y | SBrkr | 546 | 546 | 0 | 1092 | 0.0 | 0.0 | 1 | 1 | 3 | 1 | TA | 5 | Typ | 0 | NaN | NaN | NaN | NaN | 0.0 | 0.0 | NaN | NaN | Y | 0 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 6 | 2006 | WD | Normal |
| 2915 | 160 | RM | 21.0 | 1894 | Pave | NaN | Reg | Lvl | AllPub | Inside | Gtl | MeadowV | Norm | Norm | TwnhsE | 2Story | 4 | 5 | 1970 | 1970 | Gable | CompShg | CemntBd | CmentBd | None | 0.0 | TA | TA | CBlock | TA | TA | No | Rec | 252.0 | Unf | 0.0 | 294.0 | 546.0 | GasA | TA | Y | SBrkr | 546 | 546 | 0 | 1092 | 0.0 | 0.0 | 1 | 1 | 3 | 1 | TA | 6 | Typ | 0 | NaN | CarPort | 1970.0 | Unf | 1.0 | 286.0 | TA | TA | Y | 0 | 24 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 4 | 2006 | WD | Abnorml |
| 2916 | 20 | RL | 160.0 | 20000 | Pave | NaN | Reg | Lvl | AllPub | Inside | Gtl | Mitchel | Norm | Norm | 1Fam | 1Story | 5 | 7 | 1960 | 1996 | Gable | CompShg | VinylSd | VinylSd | None | 0.0 | TA | TA | CBlock | TA | TA | No | ALQ | 1224.0 | Unf | 0.0 | 0.0 | 1224.0 | GasA | Ex | Y | SBrkr | 1224 | 0 | 0 | 1224 | 1.0 | 0.0 | 1 | 0 | 4 | 1 | TA | 7 | Typ | 1 | TA | Detchd | 1960.0 | Unf | 2.0 | 576.0 | TA | TA | Y | 474 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 9 | 2006 | WD | Abnorml |
| 2917 | 85 | RL | 62.0 | 10441 | Pave | NaN | Reg | Lvl | AllPub | Inside | Gtl | Mitchel | Norm | Norm | 1Fam | SFoyer | 5 | 5 | 1992 | 1992 | Gable | CompShg | HdBoard | Wd Shng | None | 0.0 | TA | TA | PConc | Gd | TA | Av | GLQ | 337.0 | Unf | 0.0 | 575.0 | 912.0 | GasA | TA | Y | SBrkr | 970 | 0 | 0 | 970 | 0.0 | 1.0 | 1 | 0 | 3 | 1 | TA | 6 | Typ | 0 | NaN | NaN | NaN | NaN | 0.0 | 0.0 | NaN | NaN | Y | 80 | 32 | 0 | 0 | 0 | 0 | NaN | MnPrv | Shed | 700 | 7 | 2006 | WD | Normal |
| 2918 | 60 | RL | 74.0 | 9627 | Pave | NaN | Reg | Lvl | AllPub | Inside | Mod | Mitchel | Norm | Norm | 1Fam | 2Story | 7 | 5 | 1993 | 1994 | Gable | CompShg | HdBoard | HdBoard | BrkFace | 94.0 | TA | TA | PConc | Gd | TA | Av | LwQ | 758.0 | Unf | 0.0 | 238.0 | 996.0 | GasA | Ex | Y | SBrkr | 996 | 1004 | 0 | 2000 | 0.0 | 0.0 | 2 | 1 | 3 | 1 | TA | 9 | Typ | 1 | TA | Attchd | 1993.0 | Fin | 3.0 | 650.0 | TA | TA | Y | 190 | 48 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 11 | 2006 | WD | Normal |
2919 rows × 79 columns
data1.isna().sum()
MSSubClass 0 MSZoning 4 LotFrontage 486 LotArea 0 Street 0 Alley 2721 LotShape 0 LandContour 0 Utilities 2 LotConfig 0 LandSlope 0 Neighborhood 0 Condition1 0 Condition2 0 BldgType 0 HouseStyle 0 OverallQual 0 OverallCond 0 YearBuilt 0 YearRemodAdd 0 RoofStyle 0 RoofMatl 0 Exterior1st 1 Exterior2nd 1 MasVnrType 24 MasVnrArea 23 ExterQual 0 ExterCond 0 Foundation 0 BsmtQual 81 BsmtCond 82 BsmtExposure 82 BsmtFinType1 79 BsmtFinSF1 1 BsmtFinType2 80 BsmtFinSF2 1 BsmtUnfSF 1 TotalBsmtSF 1 Heating 0 HeatingQC 0 CentralAir 0 Electrical 1 1stFlrSF 0 2ndFlrSF 0 LowQualFinSF 0 GrLivArea 0 BsmtFullBath 2 BsmtHalfBath 2 FullBath 0 HalfBath 0 BedroomAbvGr 0 KitchenAbvGr 0 KitchenQual 1 TotRmsAbvGrd 0 Functional 2 Fireplaces 0 FireplaceQu 1420 GarageType 157 GarageYrBlt 159 GarageFinish 159 GarageCars 1 GarageArea 1 GarageQual 159 GarageCond 159 PavedDrive 0 WoodDeckSF 0 OpenPorchSF 0 EnclosedPorch 0 3SsnPorch 0 ScreenPorch 0 PoolArea 0 PoolQC 2909 Fence 2348 MiscFeature 2814 MiscVal 0 MoSold 0 YrSold 0 SaleType 1 SaleCondition 0 dtype: int64
data2 = data1.copy()
# msh 3arf leeh a7na hwlnaha l string bs insha'llah h3rf 2odam
# tmm 3rft hwlnaha object 3shan hwa koul al object category f 22dr amlahom bal mod 3la toul whya kant category b numbers
for feature in cat_feat:
data2[feature] = data2[feature].astype(str)
data2.select_dtypes(np.number)
| LotFrontage | LotArea | MasVnrArea | BsmtFinSF1 | BsmtFinSF2 | BsmtUnfSF | TotalBsmtSF | 1stFlrSF | 2ndFlrSF | LowQualFinSF | GrLivArea | BsmtFullBath | BsmtHalfBath | TotRmsAbvGrd | GarageYrBlt | GarageArea | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | MiscVal | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 65.0 | 8450 | 196.0 | 706.0 | 0.0 | 150.0 | 856.0 | 856 | 854 | 0 | 1710 | 1.0 | 0.0 | 8 | 2003.0 | 548.0 | 0 | 61 | 0 | 0 | 0 | 0 | 0 |
| 1 | 80.0 | 9600 | 0.0 | 978.0 | 0.0 | 284.0 | 1262.0 | 1262 | 0 | 0 | 1262 | 0.0 | 1.0 | 6 | 1976.0 | 460.0 | 298 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 68.0 | 11250 | 162.0 | 486.0 | 0.0 | 434.0 | 920.0 | 920 | 866 | 0 | 1786 | 1.0 | 0.0 | 6 | 2001.0 | 608.0 | 0 | 42 | 0 | 0 | 0 | 0 | 0 |
| 3 | 60.0 | 9550 | 0.0 | 216.0 | 0.0 | 540.0 | 756.0 | 961 | 756 | 0 | 1717 | 1.0 | 0.0 | 7 | 1998.0 | 642.0 | 0 | 35 | 272 | 0 | 0 | 0 | 0 |
| 4 | 84.0 | 14260 | 350.0 | 655.0 | 0.0 | 490.0 | 1145.0 | 1145 | 1053 | 0 | 2198 | 1.0 | 0.0 | 9 | 2000.0 | 836.0 | 192 | 84 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2914 | 21.0 | 1936 | 0.0 | 0.0 | 0.0 | 546.0 | 546.0 | 546 | 546 | 0 | 1092 | 0.0 | 0.0 | 5 | NaN | 0.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2915 | 21.0 | 1894 | 0.0 | 252.0 | 0.0 | 294.0 | 546.0 | 546 | 546 | 0 | 1092 | 0.0 | 0.0 | 6 | 1970.0 | 286.0 | 0 | 24 | 0 | 0 | 0 | 0 | 0 |
| 2916 | 160.0 | 20000 | 0.0 | 1224.0 | 0.0 | 0.0 | 1224.0 | 1224 | 0 | 0 | 1224 | 1.0 | 0.0 | 7 | 1960.0 | 576.0 | 474 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2917 | 62.0 | 10441 | 0.0 | 337.0 | 0.0 | 575.0 | 912.0 | 970 | 0 | 0 | 970 | 0.0 | 1.0 | 6 | NaN | 0.0 | 80 | 32 | 0 | 0 | 0 | 0 | 700 |
| 2918 | 74.0 | 9627 | 94.0 | 758.0 | 0.0 | 238.0 | 996.0 | 996 | 1004 | 0 | 2000 | 0.0 | 0.0 | 9 | 1993.0 | 650.0 | 190 | 48 | 0 | 0 | 0 | 0 | 0 |
2919 rows × 23 columns
# tmm 3ayz ashouf koul al object columns al 3ndhom missing values
data2.select_dtypes('object').loc[:, data2.isna().sum() > 0].columns
Index(['MSZoning', 'Alley', 'Utilities', 'Exterior1st', 'Exterior2nd',
'MasVnrType', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1',
'BsmtFinType2', 'Electrical', 'KitchenQual', 'Functional',
'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond',
'PoolQC', 'Fence', 'MiscFeature', 'SaleType'],
dtype='object')
# Impute using a constant value
# da al NA feeh bykoun leeh meaning f msh hynf3 a amllah zaee msln mfeesh krasy sofra:) f msh hynf3 amla mknha hmam y3ny
for column in [
'Alley',
'BsmtQual',
'BsmtCond',
'BsmtExposure',
'BsmtFinType1',
'BsmtFinType2',
'FireplaceQu',
'GarageType',
'GarageFinish',
'GarageQual',
'GarageCond',
'PoolQC',
'Fence',
'MiscFeature'
]:
data2[column] = data2[column].fillna("None")
# Impute using the column mode
for column in [
'MSZoning',
'Utilities',
'Exterior1st',
'Exterior2nd',
'MasVnrType',
'Electrical',
'KitchenQual',
'Functional',
'SaleType'
]:
data2[column] = data2[column].fillna(data2[column].mode()[0])
# we have no missing values in categore colums
data2.select_dtypes('object').isna().sum().sum()
0
data2.select_dtypes(np.number).isna().sum().sum()
677
data3 = data2.copy()
data3.select_dtypes(np.number).loc[:, data3.isna().sum() > 0].columns
Index(['LotFrontage', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF',
'TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath', 'GarageYrBlt',
'GarageArea'],
dtype='object')
def Knn_impute(df, na_target): # na_target -> column have null values
"""
Take data frame and column
return data frame with filling missing value in column
by using Knn algorithm
"""
df = df.copy()
numeric_df = df.select_dtypes(np.number)
non_na_columns = numeric_df.loc[:,numeric_df.isna().sum() == 0].columns
# y_train: rows that not have null values in column(na_target)
y_train = numeric_df.loc[numeric_df[na_target].isna() == False, na_target]
# X_train: columns that not have null values (non_na_column) & select from these column rows that not have null values
X_train = numeric_df.loc[numeric_df[na_target].isna() == False, non_na_columns]
# X_test: columns that not have null values (non_na_column) & select from these column rows that have null values
X_test = numeric_df.loc[numeric_df[na_target].isna() == True, non_na_columns]
"""
KNN Regressor vs classifier
The key differences are: KNN regression tries to predict the value of the output variable by using a local average.
KNN classification attempts to predict the class to which the output variable belong by computing the local probability.
"""
# fa7na mstkhdmeen KneKNeighborsRegressor 3shan hya numeric value
knn = KNeighborsRegressor()
knn.fit(X_train, y_train)
# hna hyrg3 al perdict ll null values alna kount 3ayz amlaha
y_pred = knn.predict(X_test)
df.loc[df[na_target].isna() == True, na_target] = y_pred
return df
for column in [
'LotFrontage',
'MasVnrArea',
'BsmtFinSF1',
'BsmtFinSF2',
'BsmtUnfSF',
'TotalBsmtSF',
'BsmtFullBath',
'BsmtHalfBath',
'GarageYrBlt',
'GarageArea',
]:
data3 = Knn_impute(data3,column)
data3.isna().sum().sum()
0
data4 = data3.copy()
data4['OverallQual'] = data4['OverallQual']
data4['OverallCond'] = data4['OverallCond']
data4['OverallQual-Cond'] = data4['OverallQual'].astype(int) * data4['OverallCond'].astype(int)
data4['Age'] = (data4['YrSold'].astype(int) - data4['YearBuilt'].astype(int)).astype(str)
data4['Age'] = data4['Age']
data4['RemodAge'] = data4['YrSold'].astype(int) - data4['YearRemodAdd'].astype(int)
data4['RemodAge'] = data4['RemodAge']
data4['MSSubClass'] = data4['MSSubClass'].astype(object)
data4['LotShape-Config'] = data4['LotShape'] + data4['LotConfig']
data4['Condition1-2'] = data4['Condition1'] + data4['Condition2']
data4['RoofStyle-Matl'] = data4['RoofStyle'] + data4['RoofMatl']
data4['Exterior1st-2nd-qual-cond'] = data4['Exterior1st'] + data4['Exterior2nd']+ data4['ExterQual'] + data4['ExterCond']
data4['BsmtQual-BsmtCond'] = data4['BsmtQual'] + data4['BsmtCond']
data4['Heating-HeatingQC'] = data4['Heating'] + data4['HeatingQC']
data4['Heating-CentralAir'] = data4['Heating'] + data4['CentralAir']
data4['SaleType-SaleCondition'] = data4['SaleType'] + data4['SaleCondition']
data5 = data4.copy()
print("Skewness: %f" % train0['SalePrice'].skew())
Skewness: 1.882876
# scipy.stats.skew(data5.select_dtypes(np.number))
skew_df = pd.DataFrame(data5.select_dtypes(np.number).columns, columns=['Feature'])
skew_df['Skew'] = skew_df['Feature'].apply(lambda feature: scipy.stats.skew(data5[feature]))
skew_df['Absolute Skew'] = abs(skew_df['Skew'])
skew_df['Skewed'] = skew_df['Absolute Skew'].apply(lambda x: True if x >= 0.5 else False)
skew_df
| Feature | Skew | Absolute Skew | Skewed | |
|---|---|---|---|---|
| 0 | LotFrontage | 1.341483 | 1.341483 | True |
| 1 | LotArea | 12.822431 | 12.822431 | True |
| 2 | MasVnrArea | 2.603682 | 2.603682 | True |
| 3 | BsmtFinSF1 | 1.425516 | 1.425516 | True |
| 4 | BsmtFinSF2 | 4.146111 | 4.146111 | True |
| 5 | BsmtUnfSF | 0.919193 | 0.919193 | True |
| 6 | TotalBsmtSF | 1.162791 | 1.162791 | True |
| 7 | 1stFlrSF | 1.469604 | 1.469604 | True |
| 8 | 2ndFlrSF | 0.861675 | 0.861675 | True |
| 9 | LowQualFinSF | 12.088761 | 12.088761 | True |
| 10 | GrLivArea | 1.269358 | 1.269358 | True |
| 11 | BsmtFullBath | 0.624451 | 0.624451 | True |
| 12 | BsmtHalfBath | 3.931594 | 3.931594 | True |
| 13 | TotRmsAbvGrd | 0.758367 | 0.758367 | True |
| 14 | GarageYrBlt | -0.346583 | 0.346583 | False |
| 15 | GarageArea | 0.240896 | 0.240896 | False |
| 16 | WoodDeckSF | 1.842433 | 1.842433 | True |
| 17 | OpenPorchSF | 2.535114 | 2.535114 | True |
| 18 | EnclosedPorch | 4.003891 | 4.003891 | True |
| 19 | 3SsnPorch | 11.376065 | 11.376065 | True |
| 20 | ScreenPorch | 3.946694 | 3.946694 | True |
| 21 | PoolArea | 16.898328 | 16.898328 | True |
| 22 | MiscVal | 21.947195 | 21.947195 | True |
data5.select_dtypes(np.number).hist(figsize=(16,20), bins=50, xlabelsize=8, ylabelsize=8);
skew_df['Skewed'].value_counts()
True 21 False 2 Name: Skewed, dtype: int64
data5[skew_df.query("Skewed == True")['Feature'].values].describe()
| LotFrontage | LotArea | MasVnrArea | BsmtFinSF1 | BsmtFinSF2 | BsmtUnfSF | TotalBsmtSF | 1stFlrSF | 2ndFlrSF | LowQualFinSF | GrLivArea | BsmtFullBath | BsmtHalfBath | TotRmsAbvGrd | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | MiscVal | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 | 2919.000000 |
| mean | 4.211524 | 9.094915 | 2.109776 | 4.226730 | 0.682190 | 5.627455 | 6.733148 | 7.004186 | 2.829584 | 0.077100 | 7.261502 | 0.293671 | 0.042111 | 1.986955 | 2.449664 | 2.333006 | 0.757435 | 0.066197 | 0.451214 | 0.027361 | 0.234767 |
| std | 0.341178 | 0.509882 | 2.622741 | 2.980867 | 1.879238 | 1.866527 | 1.175875 | 0.322650 | 3.277023 | 0.659472 | 0.324750 | 0.351814 | 0.167439 | 0.206864 | 2.592335 | 2.154239 | 1.771894 | 0.587089 | 1.459808 | 0.410265 | 1.240206 |
| min | 3.091042 | 7.170888 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 5.814131 | 0.000000 | 0.000000 | 5.814131 | 0.000000 | 0.000000 | 1.098612 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 4.110874 | 8.919854 | 0.000000 | 0.000000 | 0.000000 | 5.398163 | 6.677083 | 6.776507 | 0.000000 | 0.000000 | 7.027315 | 0.000000 | 0.000000 | 1.791759 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 4.262680 | 9.154193 | 0.000000 | 5.910797 | 0.000000 | 6.148468 | 6.897705 | 6.987490 | 0.000000 | 0.000000 | 7.275865 | 0.000000 | 0.000000 | 1.945910 | 0.000000 | 3.295837 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 4.396915 | 9.356257 | 5.105945 | 6.598509 | 0.000000 | 6.692083 | 7.172425 | 7.235979 | 6.558198 | 0.000000 | 7.464223 | 0.693147 | 0.000000 | 2.079442 | 5.129899 | 4.262680 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| max | 5.749393 | 12.279537 | 7.378384 | 8.638525 | 7.331060 | 7.756623 | 8.717846 | 8.536211 | 7.633370 | 6.970730 | 8.638171 | 1.386294 | 1.098612 | 2.772589 | 7.261927 | 6.610696 | 6.920672 | 6.232448 | 6.357842 | 6.685861 | 9.741027 |
# shif log by 1 np.log1p()
for column in skew_df.query("Skewed == True")['Feature'].values:
data5[column] = np.log1p(data5[column])
data5.select_dtypes(np.number).hist(figsize=(16,20), bins=50, xlabelsize=8, ylabelsize=8);
skew_df = pd.DataFrame(data5.select_dtypes(np.number).columns, columns=['Feature'])
skew_df['Skew'] = skew_df['Feature'].apply(lambda feature: scipy.stats.skew(data5[feature]))
skew_df['Absolute Skew'] = abs(skew_df['Skew'])
skew_df['Skewed'] = skew_df['Absolute Skew'].apply(lambda x: True if x >= 0.5 else False)
skew_df
| Feature | Skew | Absolute Skew | Skewed | |
|---|---|---|---|---|
| 0 | LotFrontage | -0.930231 | 0.930231 | True |
| 1 | LotArea | -0.504751 | 0.504751 | True |
| 2 | MasVnrArea | 0.511138 | 0.511138 | True |
| 3 | BsmtFinSF1 | -0.618036 | 0.618036 | True |
| 4 | BsmtFinSF2 | 2.458996 | 2.458996 | True |
| 5 | BsmtUnfSF | -2.159195 | 2.159195 | True |
| 6 | TotalBsmtSF | -4.981655 | 4.981655 | True |
| 7 | 1stFlrSF | 0.064828 | 0.064828 | False |
| 8 | 2ndFlrSF | 0.305049 | 0.305049 | False |
| 9 | LowQualFinSF | 8.557690 | 8.557690 | True |
| 10 | GrLivArea | 0.013188 | 0.013188 | False |
| 11 | BsmtFullBath | 0.424532 | 0.424532 | False |
| 12 | BsmtHalfBath | 3.774762 | 3.774762 | True |
| 13 | TotRmsAbvGrd | 0.035107 | 0.035107 | False |
| 14 | GarageYrBlt | -0.346583 | 0.346583 | False |
| 15 | GarageArea | 0.240896 | 0.240896 | False |
| 16 | WoodDeckSF | 0.158033 | 0.158033 | False |
| 17 | OpenPorchSF | -0.041797 | 0.041797 | False |
| 18 | EnclosedPorch | 1.961081 | 1.961081 | True |
| 19 | 3SsnPorch | 8.825256 | 8.825256 | True |
| 20 | ScreenPorch | 2.945905 | 2.945905 | True |
| 21 | PoolArea | 14.998335 | 14.998335 | True |
| 22 | MiscVal | 5.213984 | 5.213984 | True |
skew_df['Skewed'].value_counts() # lsa 3ndna shwyeet skeyed feature bs y3ny log transformation helwa wzbtt hgat ktera
True 14 False 9 Name: Skewed, dtype: int64
data4['MoSold'].unique()
array(['2', '5', '9', '12', '10', '8', '11', '4', '1', '7', '3', '6'],
dtype=object)
# cos transformation 0.5236 3shan akhly al one cycle mn 1 to 12 f 23dt agrb lghaeyt ml2eet al c tb2a 0.5236
data5['MoSold'] = -np.cos(0.5236 * data4['MoSold'].astype(int))
# sns.lineplot(data5,x='MoSold',y=target)
data5
| MSSubClass | MSZoning | LotFrontage | LotArea | Street | Alley | LotShape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | Condition2 | BldgType | HouseStyle | OverallQual | OverallCond | YearBuilt | YearRemodAdd | RoofStyle | RoofMatl | Exterior1st | Exterior2nd | MasVnrType | MasVnrArea | ExterQual | ExterCond | Foundation | BsmtQual | BsmtCond | BsmtExposure | BsmtFinType1 | BsmtFinSF1 | BsmtFinType2 | BsmtFinSF2 | BsmtUnfSF | TotalBsmtSF | Heating | HeatingQC | CentralAir | Electrical | 1stFlrSF | 2ndFlrSF | LowQualFinSF | GrLivArea | BsmtFullBath | BsmtHalfBath | FullBath | HalfBath | BedroomAbvGr | KitchenAbvGr | KitchenQual | TotRmsAbvGrd | Functional | Fireplaces | FireplaceQu | GarageType | GarageYrBlt | GarageFinish | GarageCars | GarageArea | GarageQual | GarageCond | PavedDrive | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | PoolQC | Fence | MiscFeature | MiscVal | MoSold | YrSold | SaleType | SaleCondition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 60 | RL | 4.189655 | 9.042040 | Pave | None | Reg | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | 7 | 5 | 2003 | 2003 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 5.283204 | Gd | TA | PConc | Gd | TA | No | GLQ | 6.561031 | Unf | 0.0 | 5.017280 | 6.753438 | GasA | Ex | Y | SBrkr | 6.753438 | 6.751101 | 0.0 | 7.444833 | 0.693147 | 0.000000 | 2 | 1 | 3 | 1 | Gd | 2.197225 | Typ | 0 | None | Attchd | 2003.0 | RFn | 2.0 | 548.0 | TA | TA | Y | 0.000000 | 4.127134 | 0.000000 | 0.0 | 0.0 | 0.0 | None | None | None | 0.000000 | -0.499998 | 2008 | WD | Normal |
| 1 | 20 | RL | 4.394449 | 9.169623 | Pave | None | Reg | Lvl | AllPub | FR2 | Gtl | Veenker | Feedr | Norm | 1Fam | 1Story | 6 | 8 | 1976 | 1976 | Gable | CompShg | MetalSd | MetalSd | None | 0.000000 | TA | TA | CBlock | Gd | TA | Gd | ALQ | 6.886532 | Unf | 0.0 | 5.652489 | 7.141245 | GasA | Ex | Y | SBrkr | 7.141245 | 0.000000 | 0.0 | 7.141245 | 0.000000 | 0.693147 | 2 | 0 | 3 | 1 | TA | 1.945910 | Typ | 1 | TA | Attchd | 1976.0 | RFn | 2.0 | 460.0 | TA | TA | Y | 5.700444 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | None | None | None | 0.000000 | 0.866028 | 2007 | WD | Normal |
| 2 | 60 | RL | 4.234107 | 9.328212 | Pave | None | IR1 | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | 7 | 5 | 2001 | 2002 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 5.093750 | Gd | TA | PConc | Gd | TA | Mn | GLQ | 6.188264 | Unf | 0.0 | 6.075346 | 6.825460 | GasA | Ex | Y | SBrkr | 6.825460 | 6.765039 | 0.0 | 7.488294 | 0.693147 | 0.000000 | 2 | 1 | 3 | 1 | Gd | 1.945910 | Typ | 1 | TA | Attchd | 2001.0 | RFn | 2.0 | 608.0 | TA | TA | Y | 0.000000 | 3.761200 | 0.000000 | 0.0 | 0.0 | 0.0 | None | None | None | 0.000000 | -0.000011 | 2008 | WD | Normal |
| 3 | 70 | RL | 4.110874 | 9.164401 | Pave | None | IR1 | Lvl | AllPub | Corner | Gtl | Crawfor | Norm | Norm | 1Fam | 2Story | 7 | 5 | 1915 | 1970 | Gable | CompShg | Wd Sdng | Wd Shng | None | 0.000000 | TA | TA | BrkTil | TA | Gd | No | ALQ | 5.379897 | Unf | 0.0 | 6.293419 | 6.629363 | GasA | Gd | Y | SBrkr | 6.869014 | 6.629363 | 0.0 | 7.448916 | 0.693147 | 0.000000 | 1 | 0 | 3 | 1 | Gd | 2.079442 | Typ | 1 | Gd | Detchd | 1998.0 | Unf | 3.0 | 642.0 | TA | TA | Y | 0.000000 | 3.583519 | 5.609472 | 0.0 | 0.0 | 0.0 | None | None | None | 0.000000 | -0.499998 | 2006 | WD | Abnorml |
| 4 | 60 | RL | 4.442651 | 9.565284 | Pave | None | IR1 | Lvl | AllPub | FR2 | Gtl | NoRidge | Norm | Norm | 1Fam | 2Story | 8 | 5 | 2000 | 2000 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 5.860786 | Gd | TA | PConc | Gd | TA | Av | GLQ | 6.486161 | Unf | 0.0 | 6.196444 | 7.044033 | GasA | Ex | Y | SBrkr | 7.044033 | 6.960348 | 0.0 | 7.695758 | 0.693147 | 0.000000 | 2 | 1 | 4 | 1 | Gd | 2.302585 | Typ | 1 | TA | Attchd | 2000.0 | RFn | 3.0 | 836.0 | TA | TA | Y | 5.262690 | 4.442651 | 0.000000 | 0.0 | 0.0 | 0.0 | None | None | None | 0.000000 | -1.000000 | 2008 | WD | Normal |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2914 | 160 | RM | 3.091042 | 7.568896 | Pave | None | Reg | Lvl | AllPub | Inside | Gtl | MeadowV | Norm | Norm | Twnhs | 2Story | 4 | 7 | 1970 | 1970 | Gable | CompShg | CemntBd | CmentBd | None | 0.000000 | TA | TA | CBlock | TA | TA | No | Unf | 0.000000 | Unf | 0.0 | 6.304449 | 6.304449 | GasA | Gd | Y | SBrkr | 6.304449 | 6.304449 | 0.0 | 6.996681 | 0.000000 | 0.000000 | 1 | 1 | 3 | 1 | TA | 1.791759 | Typ | 0 | None | None | 1973.8 | None | 0.0 | 0.0 | None | None | Y | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | None | None | None | 0.000000 | 1.000000 | 2006 | WD | Normal |
| 2915 | 160 | RM | 3.091042 | 7.546974 | Pave | None | Reg | Lvl | AllPub | Inside | Gtl | MeadowV | Norm | Norm | TwnhsE | 2Story | 4 | 5 | 1970 | 1970 | Gable | CompShg | CemntBd | CmentBd | None | 0.000000 | TA | TA | CBlock | TA | TA | No | Rec | 5.533389 | Unf | 0.0 | 5.686975 | 6.304449 | GasA | TA | Y | SBrkr | 6.304449 | 6.304449 | 0.0 | 6.996681 | 0.000000 | 0.000000 | 1 | 1 | 3 | 1 | TA | 1.945910 | Typ | 0 | None | CarPort | 1970.0 | Unf | 1.0 | 286.0 | TA | TA | Y | 0.000000 | 3.218876 | 0.000000 | 0.0 | 0.0 | 0.0 | None | None | None | 0.000000 | 0.500004 | 2006 | WD | Abnorml |
| 2916 | 20 | RL | 5.081404 | 9.903538 | Pave | None | Reg | Lvl | AllPub | Inside | Gtl | Mitchel | Norm | Norm | 1Fam | 1Story | 5 | 7 | 1960 | 1996 | Gable | CompShg | VinylSd | VinylSd | None | 0.000000 | TA | TA | CBlock | TA | TA | No | ALQ | 7.110696 | Unf | 0.0 | 0.000000 | 7.110696 | GasA | Ex | Y | SBrkr | 7.110696 | 0.000000 | 0.0 | 7.110696 | 0.693147 | 0.000000 | 1 | 0 | 4 | 1 | TA | 2.079442 | Typ | 1 | TA | Detchd | 1960.0 | Unf | 2.0 | 576.0 | TA | TA | Y | 6.163315 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | None | None | None | 0.000000 | -0.000011 | 2006 | WD | Abnorml |
| 2917 | 85 | RL | 4.143135 | 9.253591 | Pave | None | Reg | Lvl | AllPub | Inside | Gtl | Mitchel | Norm | Norm | 1Fam | SFoyer | 5 | 5 | 1992 | 1992 | Gable | CompShg | HdBoard | Wd Shng | None | 0.000000 | TA | TA | PConc | Gd | TA | Av | GLQ | 5.823046 | Unf | 0.0 | 6.356108 | 6.816736 | GasA | TA | Y | SBrkr | 6.878326 | 0.000000 | 0.0 | 6.878326 | 0.000000 | 0.693147 | 1 | 0 | 3 | 1 | TA | 1.945910 | Typ | 0 | None | None | 1966.2 | None | 0.0 | 0.0 | None | None | Y | 4.394449 | 3.496508 | 0.000000 | 0.0 | 0.0 | 0.0 | None | MnPrv | Shed | 6.552508 | 0.866021 | 2006 | WD | Normal |
| 2918 | 60 | RL | 4.317488 | 9.172431 | Pave | None | Reg | Lvl | AllPub | Inside | Mod | Mitchel | Norm | Norm | 1Fam | 2Story | 7 | 5 | 1993 | 1994 | Gable | CompShg | HdBoard | HdBoard | BrkFace | 4.553877 | TA | TA | PConc | Gd | TA | Av | LwQ | 6.632002 | Unf | 0.0 | 5.476464 | 6.904751 | GasA | Ex | Y | SBrkr | 6.904751 | 6.912743 | 0.0 | 7.601402 | 0.000000 | 0.000000 | 2 | 1 | 3 | 1 | TA | 2.302585 | Typ | 1 | TA | Attchd | 1993.0 | Fin | 3.0 | 650.0 | TA | TA | Y | 5.252273 | 3.891820 | 0.000000 | 0.0 | 0.0 | 0.0 | None | None | None | 0.000000 | -0.866032 | 2006 | WD | Normal |
2919 rows × 79 columns
data6 = data5.copy()
data6 = pd.get_dummies(data6) # one hot encodeing
data6
| LotFrontage | LotArea | MasVnrArea | BsmtFinSF1 | BsmtFinSF2 | BsmtUnfSF | TotalBsmtSF | 1stFlrSF | 2ndFlrSF | LowQualFinSF | GrLivArea | BsmtFullBath | BsmtHalfBath | TotRmsAbvGrd | GarageYrBlt | GarageArea | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | MiscVal | MoSold | MSSubClass_120 | MSSubClass_150 | MSSubClass_160 | MSSubClass_180 | MSSubClass_190 | MSSubClass_20 | MSSubClass_30 | MSSubClass_40 | MSSubClass_45 | MSSubClass_50 | MSSubClass_60 | MSSubClass_70 | MSSubClass_75 | MSSubClass_80 | MSSubClass_85 | MSSubClass_90 | MSZoning_C (all) | MSZoning_FV | MSZoning_RH | MSZoning_RL | MSZoning_RM | Street_Grvl | Street_Pave | Alley_Grvl | Alley_None | Alley_Pave | LotShape_IR1 | LotShape_IR2 | LotShape_IR3 | LotShape_Reg | LandContour_Bnk | LandContour_HLS | LandContour_Low | LandContour_Lvl | Utilities_AllPub | Utilities_NoSeWa | LotConfig_Corner | LotConfig_CulDSac | LotConfig_FR2 | LotConfig_FR3 | LotConfig_Inside | LandSlope_Gtl | LandSlope_Mod | LandSlope_Sev | Neighborhood_Blmngtn | Neighborhood_Blueste | Neighborhood_BrDale | Neighborhood_BrkSide | Neighborhood_ClearCr | Neighborhood_CollgCr | Neighborhood_Crawfor | Neighborhood_Edwards | Neighborhood_Gilbert | Neighborhood_IDOTRR | Neighborhood_MeadowV | Neighborhood_Mitchel | Neighborhood_NAmes | Neighborhood_NPkVill | Neighborhood_NWAmes | Neighborhood_NoRidge | Neighborhood_NridgHt | Neighborhood_OldTown | Neighborhood_SWISU | Neighborhood_Sawyer | Neighborhood_SawyerW | Neighborhood_Somerst | Neighborhood_StoneBr | Neighborhood_Timber | Neighborhood_Veenker | Condition1_Artery | Condition1_Feedr | Condition1_Norm | Condition1_PosA | Condition1_PosN | Condition1_RRAe | Condition1_RRAn | Condition1_RRNe | Condition1_RRNn | Condition2_Artery | Condition2_Feedr | Condition2_Norm | Condition2_PosA | Condition2_PosN | Condition2_RRAe | Condition2_RRAn | Condition2_RRNn | BldgType_1Fam | BldgType_2fmCon | BldgType_Duplex | BldgType_Twnhs | BldgType_TwnhsE | HouseStyle_1.5Fin | HouseStyle_1.5Unf | HouseStyle_1Story | HouseStyle_2.5Fin | HouseStyle_2.5Unf | HouseStyle_2Story | HouseStyle_SFoyer | HouseStyle_SLvl | OverallQual_1 | OverallQual_10 | OverallQual_2 | OverallQual_3 | OverallQual_4 | OverallQual_5 | OverallQual_6 | OverallQual_7 | OverallQual_8 | OverallQual_9 | OverallCond_1 | OverallCond_2 | OverallCond_3 | OverallCond_4 | OverallCond_5 | OverallCond_6 | OverallCond_7 | OverallCond_8 | OverallCond_9 | YearBuilt_1872 | YearBuilt_1875 | YearBuilt_1879 | YearBuilt_1880 | YearBuilt_1882 | YearBuilt_1885 | YearBuilt_1890 | YearBuilt_1892 | YearBuilt_1893 | YearBuilt_1895 | YearBuilt_1896 | YearBuilt_1898 | YearBuilt_1900 | YearBuilt_1901 | YearBuilt_1902 | YearBuilt_1904 | YearBuilt_1905 | YearBuilt_1906 | YearBuilt_1907 | YearBuilt_1908 | YearBuilt_1910 | YearBuilt_1911 | YearBuilt_1912 | YearBuilt_1913 | YearBuilt_1914 | YearBuilt_1915 | YearBuilt_1916 | YearBuilt_1917 | YearBuilt_1918 | YearBuilt_1919 | YearBuilt_1920 | YearBuilt_1921 | YearBuilt_1922 | YearBuilt_1923 | YearBuilt_1924 | YearBuilt_1925 | YearBuilt_1926 | YearBuilt_1927 | YearBuilt_1928 | YearBuilt_1929 | YearBuilt_1930 | YearBuilt_1931 | YearBuilt_1932 | YearBuilt_1934 | YearBuilt_1935 | YearBuilt_1936 | YearBuilt_1937 | YearBuilt_1938 | YearBuilt_1939 | YearBuilt_1940 | YearBuilt_1941 | YearBuilt_1942 | YearBuilt_1945 | YearBuilt_1946 | YearBuilt_1947 | YearBuilt_1948 | YearBuilt_1949 | YearBuilt_1950 | YearBuilt_1951 | YearBuilt_1952 | YearBuilt_1953 | YearBuilt_1954 | YearBuilt_1955 | YearBuilt_1956 | YearBuilt_1957 | YearBuilt_1958 | YearBuilt_1959 | YearBuilt_1960 | YearBuilt_1961 | YearBuilt_1962 | YearBuilt_1963 | YearBuilt_1964 | YearBuilt_1965 | YearBuilt_1966 | YearBuilt_1967 | YearBuilt_1968 | YearBuilt_1969 | YearBuilt_1970 | YearBuilt_1971 | YearBuilt_1972 | YearBuilt_1973 | YearBuilt_1974 | YearBuilt_1975 | YearBuilt_1976 | YearBuilt_1977 | YearBuilt_1978 | YearBuilt_1979 | YearBuilt_1980 | YearBuilt_1981 | YearBuilt_1982 | YearBuilt_1983 | YearBuilt_1984 | YearBuilt_1985 | YearBuilt_1986 | YearBuilt_1987 | YearBuilt_1988 | YearBuilt_1989 | YearBuilt_1990 | YearBuilt_1991 | YearBuilt_1992 | YearBuilt_1993 | YearBuilt_1994 | YearBuilt_1995 | YearBuilt_1996 | YearBuilt_1997 | YearBuilt_1998 | YearBuilt_1999 | YearBuilt_2000 | YearBuilt_2001 | YearBuilt_2002 | YearBuilt_2003 | YearBuilt_2004 | YearBuilt_2005 | YearBuilt_2006 | YearBuilt_2007 | YearBuilt_2008 | YearBuilt_2009 | YearBuilt_2010 | YearRemodAdd_1950 | YearRemodAdd_1951 | YearRemodAdd_1952 | YearRemodAdd_1953 | YearRemodAdd_1954 | YearRemodAdd_1955 | YearRemodAdd_1956 | YearRemodAdd_1957 | YearRemodAdd_1958 | YearRemodAdd_1959 | YearRemodAdd_1960 | YearRemodAdd_1961 | YearRemodAdd_1962 | YearRemodAdd_1963 | YearRemodAdd_1964 | YearRemodAdd_1965 | YearRemodAdd_1966 | YearRemodAdd_1967 | YearRemodAdd_1968 | YearRemodAdd_1969 | YearRemodAdd_1970 | YearRemodAdd_1971 | YearRemodAdd_1972 | YearRemodAdd_1973 | YearRemodAdd_1974 | YearRemodAdd_1975 | YearRemodAdd_1976 | YearRemodAdd_1977 | YearRemodAdd_1978 | YearRemodAdd_1979 | YearRemodAdd_1980 | YearRemodAdd_1981 | YearRemodAdd_1982 | YearRemodAdd_1983 | YearRemodAdd_1984 | YearRemodAdd_1985 | YearRemodAdd_1986 | YearRemodAdd_1987 | YearRemodAdd_1988 | YearRemodAdd_1989 | YearRemodAdd_1990 | YearRemodAdd_1991 | YearRemodAdd_1992 | YearRemodAdd_1993 | YearRemodAdd_1994 | YearRemodAdd_1995 | YearRemodAdd_1996 | YearRemodAdd_1997 | YearRemodAdd_1998 | YearRemodAdd_1999 | YearRemodAdd_2000 | YearRemodAdd_2001 | YearRemodAdd_2002 | YearRemodAdd_2003 | YearRemodAdd_2004 | YearRemodAdd_2005 | YearRemodAdd_2006 | YearRemodAdd_2007 | YearRemodAdd_2008 | YearRemodAdd_2009 | YearRemodAdd_2010 | RoofStyle_Flat | RoofStyle_Gable | RoofStyle_Gambrel | RoofStyle_Hip | RoofStyle_Mansard | RoofStyle_Shed | RoofMatl_ClyTile | RoofMatl_CompShg | RoofMatl_Membran | RoofMatl_Metal | RoofMatl_Roll | RoofMatl_Tar&Grv | RoofMatl_WdShake | RoofMatl_WdShngl | Exterior1st_AsbShng | Exterior1st_AsphShn | Exterior1st_BrkComm | Exterior1st_BrkFace | Exterior1st_CBlock | Exterior1st_CemntBd | Exterior1st_HdBoard | Exterior1st_ImStucc | Exterior1st_MetalSd | Exterior1st_Plywood | Exterior1st_Stone | Exterior1st_Stucco | Exterior1st_VinylSd | Exterior1st_Wd Sdng | Exterior1st_WdShing | Exterior2nd_AsbShng | Exterior2nd_AsphShn | Exterior2nd_Brk Cmn | Exterior2nd_BrkFace | Exterior2nd_CBlock | Exterior2nd_CmentBd | Exterior2nd_HdBoard | Exterior2nd_ImStucc | Exterior2nd_MetalSd | Exterior2nd_Other | Exterior2nd_Plywood | Exterior2nd_Stone | Exterior2nd_Stucco | Exterior2nd_VinylSd | Exterior2nd_Wd Sdng | Exterior2nd_Wd Shng | MasVnrType_BrkCmn | MasVnrType_BrkFace | MasVnrType_None | MasVnrType_Stone | ExterQual_Ex | ExterQual_Fa | ExterQual_Gd | ExterQual_TA | ExterCond_Ex | ExterCond_Fa | ExterCond_Gd | ExterCond_Po | ExterCond_TA | Foundation_BrkTil | Foundation_CBlock | Foundation_PConc | Foundation_Slab | Foundation_Stone | Foundation_Wood | BsmtQual_Ex | BsmtQual_Fa | BsmtQual_Gd | BsmtQual_None | BsmtQual_TA | BsmtCond_Fa | BsmtCond_Gd | BsmtCond_None | BsmtCond_Po | BsmtCond_TA | BsmtExposure_Av | BsmtExposure_Gd | BsmtExposure_Mn | BsmtExposure_No | BsmtExposure_None | BsmtFinType1_ALQ | BsmtFinType1_BLQ | BsmtFinType1_GLQ | BsmtFinType1_LwQ | BsmtFinType1_None | BsmtFinType1_Rec | BsmtFinType1_Unf | BsmtFinType2_ALQ | BsmtFinType2_BLQ | BsmtFinType2_GLQ | BsmtFinType2_LwQ | BsmtFinType2_None | BsmtFinType2_Rec | BsmtFinType2_Unf | Heating_Floor | Heating_GasA | Heating_GasW | Heating_Grav | Heating_OthW | Heating_Wall | HeatingQC_Ex | HeatingQC_Fa | HeatingQC_Gd | HeatingQC_Po | HeatingQC_TA | CentralAir_N | CentralAir_Y | Electrical_FuseA | Electrical_FuseF | Electrical_FuseP | Electrical_Mix | Electrical_SBrkr | FullBath_0 | FullBath_1 | FullBath_2 | FullBath_3 | FullBath_4 | HalfBath_0 | HalfBath_1 | HalfBath_2 | BedroomAbvGr_0 | BedroomAbvGr_1 | BedroomAbvGr_2 | BedroomAbvGr_3 | BedroomAbvGr_4 | BedroomAbvGr_5 | BedroomAbvGr_6 | BedroomAbvGr_8 | KitchenAbvGr_0 | KitchenAbvGr_1 | KitchenAbvGr_2 | KitchenAbvGr_3 | KitchenQual_Ex | KitchenQual_Fa | KitchenQual_Gd | KitchenQual_TA | Functional_Maj1 | Functional_Maj2 | Functional_Min1 | Functional_Min2 | Functional_Mod | Functional_Sev | Functional_Typ | Fireplaces_0 | Fireplaces_1 | Fireplaces_2 | Fireplaces_3 | Fireplaces_4 | FireplaceQu_Ex | FireplaceQu_Fa | FireplaceQu_Gd | FireplaceQu_None | FireplaceQu_Po | FireplaceQu_TA | GarageType_2Types | GarageType_Attchd | GarageType_Basment | GarageType_BuiltIn | GarageType_CarPort | GarageType_Detchd | GarageType_None | GarageFinish_Fin | GarageFinish_None | GarageFinish_RFn | GarageFinish_Unf | GarageCars_0.0 | GarageCars_1.0 | GarageCars_2.0 | GarageCars_3.0 | GarageCars_4.0 | GarageCars_5.0 | GarageCars_nan | GarageQual_Ex | GarageQual_Fa | GarageQual_Gd | GarageQual_None | GarageQual_Po | GarageQual_TA | GarageCond_Ex | GarageCond_Fa | GarageCond_Gd | GarageCond_None | GarageCond_Po | GarageCond_TA | PavedDrive_N | PavedDrive_P | PavedDrive_Y | PoolQC_Ex | PoolQC_Fa | PoolQC_Gd | PoolQC_None | Fence_GdPrv | Fence_GdWo | Fence_MnPrv | Fence_MnWw | Fence_None | MiscFeature_Gar2 | MiscFeature_None | MiscFeature_Othr | MiscFeature_Shed | MiscFeature_TenC | YrSold_2006 | YrSold_2007 | YrSold_2008 | YrSold_2009 | YrSold_2010 | SaleType_COD | SaleType_CWD | SaleType_Con | SaleType_ConLD | SaleType_ConLI | SaleType_ConLw | SaleType_New | SaleType_Oth | SaleType_WD | SaleCondition_Abnorml | SaleCondition_AdjLand | SaleCondition_Alloca | SaleCondition_Family | SaleCondition_Normal | SaleCondition_Partial | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 4.189655 | 9.042040 | 5.283204 | 6.561031 | 0.0 | 5.017280 | 6.753438 | 6.753438 | 6.751101 | 0.0 | 7.444833 | 0.693147 | 0.000000 | 2.197225 | 2003.0 | 548.0 | 0.000000 | 4.127134 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | -0.499998 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1 | 4.394449 | 9.169623 | 0.000000 | 6.886532 | 0.0 | 5.652489 | 7.141245 | 7.141245 | 0.000000 | 0.0 | 7.141245 | 0.000000 | 0.693147 | 1.945910 | 1976.0 | 460.0 | 5.700444 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.866028 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 2 | 4.234107 | 9.328212 | 5.093750 | 6.188264 | 0.0 | 6.075346 | 6.825460 | 6.825460 | 6.765039 | 0.0 | 7.488294 | 0.693147 | 0.000000 | 1.945910 | 2001.0 | 608.0 | 0.000000 | 3.761200 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | -0.000011 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 3 | 4.110874 | 9.164401 | 0.000000 | 5.379897 | 0.0 | 6.293419 | 6.629363 | 6.869014 | 6.629363 | 0.0 | 7.448916 | 0.693147 | 0.000000 | 2.079442 | 1998.0 | 642.0 | 0.000000 | 3.583519 | 5.609472 | 0.0 | 0.0 | 0.0 | 0.000000 | -0.499998 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| 4 | 4.442651 | 9.565284 | 5.860786 | 6.486161 | 0.0 | 6.196444 | 7.044033 | 7.044033 | 6.960348 | 0.0 | 7.695758 | 0.693147 | 0.000000 | 2.302585 | 2000.0 | 836.0 | 5.262690 | 4.442651 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | -1.000000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
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| 2914 | 3.091042 | 7.568896 | 0.000000 | 0.000000 | 0.0 | 6.304449 | 6.304449 | 6.304449 | 6.304449 | 0.0 | 6.996681 | 0.000000 | 0.000000 | 1.791759 | 1973.8 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 1.000000 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 2915 | 3.091042 | 7.546974 | 0.000000 | 5.533389 | 0.0 | 5.686975 | 6.304449 | 6.304449 | 6.304449 | 0.0 | 6.996681 | 0.000000 | 0.000000 | 1.945910 | 1970.0 | 286.0 | 0.000000 | 3.218876 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.500004 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| 2916 | 5.081404 | 9.903538 | 0.000000 | 7.110696 | 0.0 | 0.000000 | 7.110696 | 7.110696 | 0.000000 | 0.0 | 7.110696 | 0.693147 | 0.000000 | 2.079442 | 1960.0 | 576.0 | 6.163315 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | -0.000011 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| 2917 | 4.143135 | 9.253591 | 0.000000 | 5.823046 | 0.0 | 6.356108 | 6.816736 | 6.878326 | 0.000000 | 0.0 | 6.878326 | 0.000000 | 0.693147 | 1.945910 | 1966.2 | 0.0 | 4.394449 | 3.496508 | 0.000000 | 0.0 | 0.0 | 0.0 | 6.552508 | 0.866021 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 2918 | 4.317488 | 9.172431 | 4.553877 | 6.632002 | 0.0 | 5.476464 | 6.904751 | 6.904751 | 6.912743 | 0.0 | 7.601402 | 0.000000 | 0.000000 | 2.302585 | 1993.0 | 650.0 | 5.252273 | 3.891820 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | -0.866032 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
2919 rows × 541 columns
data7 = data6.copy()
# https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
scaler = StandardScaler()
scaler.fit(data7)
data6 = pd.DataFrame(scaler.transform(data7), index=data6.index, columns=data6.columns)
data7
| LotFrontage | LotArea | MasVnrArea | BsmtFinSF1 | BsmtFinSF2 | BsmtUnfSF | TotalBsmtSF | 1stFlrSF | 2ndFlrSF | LowQualFinSF | GrLivArea | BsmtFullBath | BsmtHalfBath | TotRmsAbvGrd | GarageYrBlt | GarageArea | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | MiscVal | MoSold | MSSubClass_120 | MSSubClass_150 | MSSubClass_160 | MSSubClass_180 | MSSubClass_190 | MSSubClass_20 | MSSubClass_30 | MSSubClass_40 | MSSubClass_45 | MSSubClass_50 | MSSubClass_60 | MSSubClass_70 | MSSubClass_75 | MSSubClass_80 | MSSubClass_85 | MSSubClass_90 | MSZoning_C (all) | MSZoning_FV | MSZoning_RH | MSZoning_RL | MSZoning_RM | Street_Grvl | Street_Pave | Alley_Grvl | Alley_None | Alley_Pave | LotShape_IR1 | LotShape_IR2 | LotShape_IR3 | LotShape_Reg | LandContour_Bnk | LandContour_HLS | LandContour_Low | LandContour_Lvl | Utilities_AllPub | Utilities_NoSeWa | LotConfig_Corner | LotConfig_CulDSac | LotConfig_FR2 | LotConfig_FR3 | LotConfig_Inside | LandSlope_Gtl | LandSlope_Mod | LandSlope_Sev | Neighborhood_Blmngtn | Neighborhood_Blueste | Neighborhood_BrDale | Neighborhood_BrkSide | Neighborhood_ClearCr | Neighborhood_CollgCr | Neighborhood_Crawfor | Neighborhood_Edwards | Neighborhood_Gilbert | Neighborhood_IDOTRR | Neighborhood_MeadowV | Neighborhood_Mitchel | Neighborhood_NAmes | Neighborhood_NPkVill | Neighborhood_NWAmes | Neighborhood_NoRidge | Neighborhood_NridgHt | Neighborhood_OldTown | Neighborhood_SWISU | Neighborhood_Sawyer | Neighborhood_SawyerW | Neighborhood_Somerst | Neighborhood_StoneBr | Neighborhood_Timber | Neighborhood_Veenker | Condition1_Artery | Condition1_Feedr | Condition1_Norm | Condition1_PosA | Condition1_PosN | Condition1_RRAe | Condition1_RRAn | Condition1_RRNe | Condition1_RRNn | Condition2_Artery | Condition2_Feedr | Condition2_Norm | Condition2_PosA | Condition2_PosN | Condition2_RRAe | Condition2_RRAn | Condition2_RRNn | BldgType_1Fam | BldgType_2fmCon | BldgType_Duplex | BldgType_Twnhs | BldgType_TwnhsE | HouseStyle_1.5Fin | HouseStyle_1.5Unf | HouseStyle_1Story | HouseStyle_2.5Fin | HouseStyle_2.5Unf | HouseStyle_2Story | HouseStyle_SFoyer | HouseStyle_SLvl | OverallQual_1 | OverallQual_10 | OverallQual_2 | OverallQual_3 | OverallQual_4 | OverallQual_5 | OverallQual_6 | OverallQual_7 | OverallQual_8 | OverallQual_9 | OverallCond_1 | OverallCond_2 | OverallCond_3 | OverallCond_4 | OverallCond_5 | OverallCond_6 | OverallCond_7 | OverallCond_8 | OverallCond_9 | YearBuilt_1872 | YearBuilt_1875 | YearBuilt_1879 | YearBuilt_1880 | YearBuilt_1882 | YearBuilt_1885 | YearBuilt_1890 | YearBuilt_1892 | YearBuilt_1893 | YearBuilt_1895 | YearBuilt_1896 | YearBuilt_1898 | YearBuilt_1900 | YearBuilt_1901 | YearBuilt_1902 | YearBuilt_1904 | YearBuilt_1905 | YearBuilt_1906 | YearBuilt_1907 | YearBuilt_1908 | YearBuilt_1910 | YearBuilt_1911 | YearBuilt_1912 | YearBuilt_1913 | YearBuilt_1914 | YearBuilt_1915 | YearBuilt_1916 | YearBuilt_1917 | YearBuilt_1918 | YearBuilt_1919 | YearBuilt_1920 | YearBuilt_1921 | YearBuilt_1922 | YearBuilt_1923 | YearBuilt_1924 | YearBuilt_1925 | YearBuilt_1926 | YearBuilt_1927 | YearBuilt_1928 | YearBuilt_1929 | YearBuilt_1930 | YearBuilt_1931 | YearBuilt_1932 | YearBuilt_1934 | YearBuilt_1935 | YearBuilt_1936 | YearBuilt_1937 | YearBuilt_1938 | YearBuilt_1939 | YearBuilt_1940 | YearBuilt_1941 | YearBuilt_1942 | YearBuilt_1945 | YearBuilt_1946 | YearBuilt_1947 | YearBuilt_1948 | YearBuilt_1949 | YearBuilt_1950 | YearBuilt_1951 | YearBuilt_1952 | YearBuilt_1953 | YearBuilt_1954 | YearBuilt_1955 | YearBuilt_1956 | YearBuilt_1957 | YearBuilt_1958 | YearBuilt_1959 | YearBuilt_1960 | YearBuilt_1961 | YearBuilt_1962 | YearBuilt_1963 | YearBuilt_1964 | YearBuilt_1965 | YearBuilt_1966 | YearBuilt_1967 | YearBuilt_1968 | YearBuilt_1969 | YearBuilt_1970 | YearBuilt_1971 | YearBuilt_1972 | YearBuilt_1973 | YearBuilt_1974 | YearBuilt_1975 | YearBuilt_1976 | YearBuilt_1977 | YearBuilt_1978 | YearBuilt_1979 | YearBuilt_1980 | YearBuilt_1981 | YearBuilt_1982 | YearBuilt_1983 | YearBuilt_1984 | YearBuilt_1985 | YearBuilt_1986 | YearBuilt_1987 | YearBuilt_1988 | YearBuilt_1989 | YearBuilt_1990 | YearBuilt_1991 | YearBuilt_1992 | YearBuilt_1993 | YearBuilt_1994 | YearBuilt_1995 | YearBuilt_1996 | YearBuilt_1997 | YearBuilt_1998 | YearBuilt_1999 | YearBuilt_2000 | YearBuilt_2001 | YearBuilt_2002 | YearBuilt_2003 | YearBuilt_2004 | YearBuilt_2005 | YearBuilt_2006 | YearBuilt_2007 | YearBuilt_2008 | YearBuilt_2009 | YearBuilt_2010 | YearRemodAdd_1950 | YearRemodAdd_1951 | YearRemodAdd_1952 | YearRemodAdd_1953 | YearRemodAdd_1954 | YearRemodAdd_1955 | YearRemodAdd_1956 | YearRemodAdd_1957 | YearRemodAdd_1958 | YearRemodAdd_1959 | YearRemodAdd_1960 | YearRemodAdd_1961 | YearRemodAdd_1962 | YearRemodAdd_1963 | YearRemodAdd_1964 | YearRemodAdd_1965 | YearRemodAdd_1966 | YearRemodAdd_1967 | YearRemodAdd_1968 | YearRemodAdd_1969 | YearRemodAdd_1970 | YearRemodAdd_1971 | YearRemodAdd_1972 | YearRemodAdd_1973 | YearRemodAdd_1974 | YearRemodAdd_1975 | YearRemodAdd_1976 | YearRemodAdd_1977 | YearRemodAdd_1978 | YearRemodAdd_1979 | YearRemodAdd_1980 | YearRemodAdd_1981 | YearRemodAdd_1982 | YearRemodAdd_1983 | YearRemodAdd_1984 | YearRemodAdd_1985 | YearRemodAdd_1986 | YearRemodAdd_1987 | YearRemodAdd_1988 | YearRemodAdd_1989 | YearRemodAdd_1990 | YearRemodAdd_1991 | YearRemodAdd_1992 | YearRemodAdd_1993 | YearRemodAdd_1994 | YearRemodAdd_1995 | YearRemodAdd_1996 | YearRemodAdd_1997 | YearRemodAdd_1998 | YearRemodAdd_1999 | YearRemodAdd_2000 | YearRemodAdd_2001 | YearRemodAdd_2002 | YearRemodAdd_2003 | YearRemodAdd_2004 | YearRemodAdd_2005 | YearRemodAdd_2006 | YearRemodAdd_2007 | YearRemodAdd_2008 | YearRemodAdd_2009 | YearRemodAdd_2010 | RoofStyle_Flat | RoofStyle_Gable | RoofStyle_Gambrel | RoofStyle_Hip | RoofStyle_Mansard | RoofStyle_Shed | RoofMatl_ClyTile | RoofMatl_CompShg | RoofMatl_Membran | RoofMatl_Metal | RoofMatl_Roll | RoofMatl_Tar&Grv | RoofMatl_WdShake | RoofMatl_WdShngl | Exterior1st_AsbShng | Exterior1st_AsphShn | Exterior1st_BrkComm | Exterior1st_BrkFace | Exterior1st_CBlock | Exterior1st_CemntBd | Exterior1st_HdBoard | Exterior1st_ImStucc | Exterior1st_MetalSd | Exterior1st_Plywood | Exterior1st_Stone | Exterior1st_Stucco | Exterior1st_VinylSd | Exterior1st_Wd Sdng | Exterior1st_WdShing | Exterior2nd_AsbShng | Exterior2nd_AsphShn | Exterior2nd_Brk Cmn | Exterior2nd_BrkFace | Exterior2nd_CBlock | Exterior2nd_CmentBd | Exterior2nd_HdBoard | Exterior2nd_ImStucc | Exterior2nd_MetalSd | Exterior2nd_Other | Exterior2nd_Plywood | Exterior2nd_Stone | Exterior2nd_Stucco | Exterior2nd_VinylSd | Exterior2nd_Wd Sdng | Exterior2nd_Wd Shng | MasVnrType_BrkCmn | MasVnrType_BrkFace | MasVnrType_None | MasVnrType_Stone | ExterQual_Ex | ExterQual_Fa | ExterQual_Gd | ExterQual_TA | ExterCond_Ex | ExterCond_Fa | ExterCond_Gd | ExterCond_Po | ExterCond_TA | Foundation_BrkTil | Foundation_CBlock | Foundation_PConc | Foundation_Slab | Foundation_Stone | Foundation_Wood | BsmtQual_Ex | BsmtQual_Fa | BsmtQual_Gd | BsmtQual_None | BsmtQual_TA | BsmtCond_Fa | BsmtCond_Gd | BsmtCond_None | BsmtCond_Po | BsmtCond_TA | BsmtExposure_Av | BsmtExposure_Gd | BsmtExposure_Mn | BsmtExposure_No | BsmtExposure_None | BsmtFinType1_ALQ | BsmtFinType1_BLQ | BsmtFinType1_GLQ | BsmtFinType1_LwQ | BsmtFinType1_None | BsmtFinType1_Rec | BsmtFinType1_Unf | BsmtFinType2_ALQ | BsmtFinType2_BLQ | BsmtFinType2_GLQ | BsmtFinType2_LwQ | BsmtFinType2_None | BsmtFinType2_Rec | BsmtFinType2_Unf | Heating_Floor | Heating_GasA | Heating_GasW | Heating_Grav | Heating_OthW | Heating_Wall | HeatingQC_Ex | HeatingQC_Fa | HeatingQC_Gd | HeatingQC_Po | HeatingQC_TA | CentralAir_N | CentralAir_Y | Electrical_FuseA | Electrical_FuseF | Electrical_FuseP | Electrical_Mix | Electrical_SBrkr | FullBath_0 | FullBath_1 | FullBath_2 | FullBath_3 | FullBath_4 | HalfBath_0 | HalfBath_1 | HalfBath_2 | BedroomAbvGr_0 | BedroomAbvGr_1 | BedroomAbvGr_2 | BedroomAbvGr_3 | BedroomAbvGr_4 | BedroomAbvGr_5 | BedroomAbvGr_6 | BedroomAbvGr_8 | KitchenAbvGr_0 | KitchenAbvGr_1 | KitchenAbvGr_2 | KitchenAbvGr_3 | KitchenQual_Ex | KitchenQual_Fa | KitchenQual_Gd | KitchenQual_TA | Functional_Maj1 | Functional_Maj2 | Functional_Min1 | Functional_Min2 | Functional_Mod | Functional_Sev | Functional_Typ | Fireplaces_0 | Fireplaces_1 | Fireplaces_2 | Fireplaces_3 | Fireplaces_4 | FireplaceQu_Ex | FireplaceQu_Fa | FireplaceQu_Gd | FireplaceQu_None | FireplaceQu_Po | FireplaceQu_TA | GarageType_2Types | GarageType_Attchd | GarageType_Basment | GarageType_BuiltIn | GarageType_CarPort | GarageType_Detchd | GarageType_None | GarageFinish_Fin | GarageFinish_None | GarageFinish_RFn | GarageFinish_Unf | GarageCars_0.0 | GarageCars_1.0 | GarageCars_2.0 | GarageCars_3.0 | GarageCars_4.0 | GarageCars_5.0 | GarageCars_nan | GarageQual_Ex | GarageQual_Fa | GarageQual_Gd | GarageQual_None | GarageQual_Po | GarageQual_TA | GarageCond_Ex | GarageCond_Fa | GarageCond_Gd | GarageCond_None | GarageCond_Po | GarageCond_TA | PavedDrive_N | PavedDrive_P | PavedDrive_Y | PoolQC_Ex | PoolQC_Fa | PoolQC_Gd | PoolQC_None | Fence_GdPrv | Fence_GdWo | Fence_MnPrv | Fence_MnWw | Fence_None | MiscFeature_Gar2 | MiscFeature_None | MiscFeature_Othr | MiscFeature_Shed | MiscFeature_TenC | YrSold_2006 | YrSold_2007 | YrSold_2008 | YrSold_2009 | YrSold_2010 | SaleType_COD | SaleType_CWD | SaleType_Con | SaleType_ConLD | SaleType_ConLI | SaleType_ConLw | SaleType_New | SaleType_Oth | SaleType_WD | SaleCondition_Abnorml | SaleCondition_AdjLand | SaleCondition_Alloca | SaleCondition_Family | SaleCondition_Normal | SaleCondition_Partial | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 4.189655 | 9.042040 | 5.283204 | 6.561031 | 0.0 | 5.017280 | 6.753438 | 6.753438 | 6.751101 | 0.0 | 7.444833 | 0.693147 | 0.000000 | 2.197225 | 2003.0 | 548.0 | 0.000000 | 4.127134 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | -0.499998 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1 | 4.394449 | 9.169623 | 0.000000 | 6.886532 | 0.0 | 5.652489 | 7.141245 | 7.141245 | 0.000000 | 0.0 | 7.141245 | 0.000000 | 0.693147 | 1.945910 | 1976.0 | 460.0 | 5.700444 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.866028 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 2 | 4.234107 | 9.328212 | 5.093750 | 6.188264 | 0.0 | 6.075346 | 6.825460 | 6.825460 | 6.765039 | 0.0 | 7.488294 | 0.693147 | 0.000000 | 1.945910 | 2001.0 | 608.0 | 0.000000 | 3.761200 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | -0.000011 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 3 | 4.110874 | 9.164401 | 0.000000 | 5.379897 | 0.0 | 6.293419 | 6.629363 | 6.869014 | 6.629363 | 0.0 | 7.448916 | 0.693147 | 0.000000 | 2.079442 | 1998.0 | 642.0 | 0.000000 | 3.583519 | 5.609472 | 0.0 | 0.0 | 0.0 | 0.000000 | -0.499998 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| 4 | 4.442651 | 9.565284 | 5.860786 | 6.486161 | 0.0 | 6.196444 | 7.044033 | 7.044033 | 6.960348 | 0.0 | 7.695758 | 0.693147 | 0.000000 | 2.302585 | 2000.0 | 836.0 | 5.262690 | 4.442651 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | -1.000000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
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| 2914 | 3.091042 | 7.568896 | 0.000000 | 0.000000 | 0.0 | 6.304449 | 6.304449 | 6.304449 | 6.304449 | 0.0 | 6.996681 | 0.000000 | 0.000000 | 1.791759 | 1973.8 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 1.000000 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 2915 | 3.091042 | 7.546974 | 0.000000 | 5.533389 | 0.0 | 5.686975 | 6.304449 | 6.304449 | 6.304449 | 0.0 | 6.996681 | 0.000000 | 0.000000 | 1.945910 | 1970.0 | 286.0 | 0.000000 | 3.218876 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.500004 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| 2916 | 5.081404 | 9.903538 | 0.000000 | 7.110696 | 0.0 | 0.000000 | 7.110696 | 7.110696 | 0.000000 | 0.0 | 7.110696 | 0.693147 | 0.000000 | 2.079442 | 1960.0 | 576.0 | 6.163315 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | -0.000011 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| 2917 | 4.143135 | 9.253591 | 0.000000 | 5.823046 | 0.0 | 6.356108 | 6.816736 | 6.878326 | 0.000000 | 0.0 | 6.878326 | 0.000000 | 0.693147 | 1.945910 | 1966.2 | 0.0 | 4.394449 | 3.496508 | 0.000000 | 0.0 | 0.0 | 0.0 | 6.552508 | 0.866021 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 2918 | 4.317488 | 9.172431 | 4.553877 | 6.632002 | 0.0 | 5.476464 | 6.904751 | 6.904751 | 6.912743 | 0.0 | 7.601402 | 0.000000 | 0.000000 | 2.302585 | 1993.0 | 650.0 | 5.252273 | 3.891820 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | -0.866032 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
2919 rows × 541 columns
data8 = data7.copy()
plt.figure(figsize=(20,10))
plt.subplot(1, 2, 1)
sns.distplot(target, kde=True, fit=scipy.stats.norm)
plt.title("Without log transform")
plt.subplot(1, 2, 2)
# wa7na mssh khyfeen mn al log 3shan 22l haga fal target greater than zero f msh htbooz
sns.distplot(np.log(target), kde=True, fit=scipy.stats.norm)
plt.xlabel('Log SalePrice')
plt.title("With log transform")
plt.show();
log_target = np.log(target)
log_target
# lw 3ayz arg3 ll main target exp al target y3ny 3ks al log wkhlas afhm b2a
# np.exp(log_target)
0 12.247694
1 12.109011
2 12.317167
3 11.849398
4 12.429216
...
1455 12.072541
1456 12.254863
1457 12.493130
1458 11.864462
1459 11.901583
Name: SalePrice, Length: 1460, dtype: float64
# print(train0.shape)
# print(test0.shape)
# print(data6.shape)
print(data8.shape)
print(train0.shape)
print(test0.shape)
(2919, 541) (1460, 81) (1459, 80)
train_final = data7.loc[:train0.index.max(), :].copy()
test_final = data7.loc[train0.index.max()+1:, :].reset_index(drop=True).copy()
print(train_final.shape)
print(test_final.shape)
(1460, 541) (1459, 541)
# _ = setup(data = pd.concat([train_final, log_target], axis=1), target='SalePrice')
| Description | Value | |
|---|---|---|
| 0 | Session id | 7468 |
| 1 | Target | SalePrice |
| 2 | Target type | Regression |
| 3 | Original data shape | (1460, 542) |
| 4 | Transformed data shape | (1460, 542) |
| 5 | Transformed train set shape | (1021, 542) |
| 6 | Transformed test set shape | (439, 542) |
| 7 | Numeric features | 541 |
| 8 | Preprocess | True |
| 9 | Imputation type | simple |
| 10 | Numeric imputation | mean |
| 11 | Categorical imputation | mode |
| 12 | Fold Generator | KFold |
| 13 | Fold Number | 10 |
| 14 | CPU Jobs | -1 |
| 15 | Use GPU | False |
| 16 | Log Experiment | False |
| 17 | Experiment Name | reg-default-name |
| 18 | USI | ca75 |
# https://towardsdatascience.com/5-things-you-are-doing-wrong-in-pycaret-e01981575d2a
# top10models = compare_models(n_select=10)
# doul a7sn 5 model momken astkhdmhom ll data bt3ty bs tb3n wla wa7d fehom drsnhom f hnshoof hn3ml eh
| Initiated | . . . . . . . . . . . . . . . . . . | 01:12:49 |
|---|---|---|
| Status | . . . . . . . . . . . . . . . . . . | Loading Dependencies |
| Estimator | . . . . . . . . . . . . . . . . . . | Compiling Library |
# predict_model(top5[0])
# top5
print("Best Model ",top10models[0])
Best Model <catboost.core.CatBoostRegressor object at 0x000001AE3B5814B0>
#Using CatBoost since that it had best results
model = CatBoostRegressor(random_state=42,verbose=0,iterations=300,depth=6,learning_rate=0.1,l2_leaf_reg=1)
model.fit(train_final,log_target)
final_predict = np.exp(model.predict(test_final))
# Using cat regressor gives us the best results
# baseline_model = CatBoostRegressor(verbose=0)
# baseline_model.fit(train_final, log_target)
<catboost.core.CatBoostRegressor at 0x1ae3ba09a20>
# https://scikit-learn.org/stable/modules/model_evaluation.html
# kf = KFold(n_splits=10)
# results = cross_val_score(baseline_model, train_final, log_target, scoring = 'neg_mean_squared_error', cv =kf)
# print(len(log_target))
# print(results)
# r2_score(log_target, results)
# -results
[-0.01248682 -0.00929631 -0.01126055 -0.02214197 -0.02001481 -0.00962841 -0.01471818 -0.00903592 -0.01462041 -0.01563809]
# np.exp(np.sqrt(np.mean(-results)))
1.1250539770366386
# final_predict = baseline_model.predict(test_final)
# final_true = sample_submission['SalePrice']
# print(final_predict.shape)
# print(final_true.shape)
# final_predict = np.exp(final_predict)
# final_true = np.log1p(final_true)
# print(np.array(final_true))
# print(final_predict)
# final_predict
# print("r2 = ", r2_score(final_true,final_predict))
# print("mean square error= ", mean_squared_error(final_true,final_predict))
# print("mean absolute error= ",mean_absolute_error(final_true, final_predict))
# print(final_predict.shape)
# print(log_target.shape)
# """
# 0 169277.052498
# 1 187758.393989
# 2 183583.683570
# 3 179317.477511
# 4 150730.079977
# ...
# 1454 167081.220949
# 1455 164788.778231
# 1456 219222.423400
# 1457 184924.279659
# 1458 187741.866657
# """
# plt.figure(figsize=(5,2.5))
# sns.displot(-results, bins = 10, kde=True)
# plt.show()
# lr_model = LinearRegression()
# lr_model.fit(train_final, log_target)
# kf = KFold(n_splits=10)
# results = cross_val_score(lr_model, train_final, log_target, scoring = 'neg_mean_squared_error', cv=kf)
# np.exp(np.sqrt(np.mean(-results)))
# np.mean(results)
# plt.figure(figsize=(10,5))
# sns.displot(-results, bins = 10, kde=True)
# sample_submission
test_ids
0 1461
1 1462
2 1463
3 1464
4 1465
...
1454 2915
1455 2916
1456 2917
1457 2918
1458 2919
Name: Id, Length: 1459, dtype: int64
# final_predict = baseline_model.predict(test_final)
# r2_score(log_target,final_predict)
# print(final_predict.shape)
# print(log_target.shape)
(1459,) (1460,)
submission = pd.concat([test_ids.astype("Int32"), pd.Series(final_predict, name='SalePrice')], axis =1)
submission
| Id | SalePrice | |
|---|---|---|
| 0 | 1461 | 126915.405899 |
| 1 | 1462 | 162756.271598 |
| 2 | 1463 | 195110.529369 |
| 3 | 1464 | 202098.147983 |
| 4 | 1465 | 183570.649343 |
| ... | ... | ... |
| 1454 | 2915 | 81548.277094 |
| 1455 | 2916 | 81224.552519 |
| 1456 | 2917 | 156920.500310 |
| 1457 | 2918 | 109983.489207 |
| 1458 | 2919 | 214292.241544 |
1459 rows × 2 columns
submission.dtypes
Id Int32 SalePrice float64 dtype: object
# submission['Id'] = test
# submission["Id"] = submission["Id"].astype(int)
submission.to_csv('./submission.csv', index=False, header=True)